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Journal articles on the topic 'Point cloud color'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Ishida, Yuki, Yoshitsugu Manabe, and Noriko Yata. "Colored Point Cloud Completion for a Head Using Adversarial Rendered Image Loss." Journal of Imaging 8, no. 5 (2022): 125. http://dx.doi.org/10.3390/jimaging8050125.

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Recent advances in depth measurement and its utilization have made point cloud processing more critical. Additionally, the human head is essential for communication, and its three-dimensional data are expected to be utilized in this regard. However, a single RGB-Depth (RGBD) camera is prone to occlusion and depth measurement failure for dark hair colors such as black hair. Recently, point cloud completion, where an entire point cloud is estimated and generated from a partial point cloud, has been studied, but only the shape is learned, rather than the completion of colored point clouds. Thus,

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Ishida, Yuki, Yoshitsugu Manabe, and Noriko Yata. "Colored Point Cloud Completion for a Head Using Adversarial Rendered Image Loss." Journal of Imaging 8, no. 5 (2022): 125. http://dx.doi.org/10.3390/jimaging8050125.

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Recent advances in depth measurement and its utilization have made point cloud processing more critical. Additionally, the human head is essential for communication, and its three-dimensional data are expected to be utilized in this regard. However, a single RGB-Depth (RGBD) camera is prone to occlusion and depth measurement failure for dark hair colors such as black hair. Recently, point cloud completion, where an entire point cloud is estimated and generated from a partial point cloud, has been studied, but only the shape is learned, rather than the completion of colored point clouds. Thus,

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Yamakawa, T., K. Fukano, R. Onodera, and H. Masuda. "REFINEMENT OF COLORED MOBILE MAPPING DATA USING INTENSITY IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-1 (June 2, 2016): 167–73. http://dx.doi.org/10.5194/isprsannals-iii-1-167-2016.

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Mobile mapping systems (MMS) can capture dense point-clouds of urban scenes. For visualizing realistic scenes using point-clouds, RGB colors have to be added to point-clouds. To generate colored point-clouds in a post-process, each point is projected onto camera images and a RGB color is copied to the point at the projected position. However, incorrect colors are often added to point-clouds because of the misalignment of laser scanners, the calibration errors of cameras and laser scanners, or the failure of GPS acquisition. In this paper, we propose a new method to correct RGB colors of point-

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Yamakawa, T., K. Fukano, R. Onodera, and H. Masuda. "REFINEMENT OF COLORED MOBILE MAPPING DATA USING INTENSITY IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-1 (June 2, 2016): 167–73. http://dx.doi.org/10.5194/isprs-annals-iii-1-167-2016.

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Mobile mapping systems (MMS) can capture dense point-clouds of urban scenes. For visualizing realistic scenes using point-clouds, RGB colors have to be added to point-clouds. To generate colored point-clouds in a post-process, each point is projected onto camera images and a RGB color is copied to the point at the projected position. However, incorrect colors are often added to point-clouds because of the misalignment of laser scanners, the calibration errors of cameras and laser scanners, or the failure of GPS acquisition. In this paper, we propose a new method to correct RGB colors of point-

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Ren, Siyu, Xiaodong Chen, Huaiyu Cai, Yi Wang, Haitao Liang, and Haotian Li. "Color Point Cloud Registration Algorithm Based on Hue." Applied Sciences 11, no. 12 (2021): 5431. http://dx.doi.org/10.3390/app11125431.

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ICP is a well-known method for point cloud registration but it only uses geometric information to do this, which will result in bad results in some similar structures. Adding color information when registering will improve the performance. However, color information of point cloud, such as gray, varies differently under different lighting conditions. Using gray as the color information to register can cause large errors and even wrong results. To solve this problem, we propose a color point cloud registration algorithm based on hue, which has good robustness at different lighting conditions. W

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Lokesh M R, Anushitha K, Ashok D, Deepak Raj K, and Harshitha K. "3D Point-Cloud Processing Using Panoramic Images for Object Detection." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (2024): 186–98. http://dx.doi.org/10.32628/cseit2410318.

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The Remote sensing application plays a major role in real-world critical application projects. The research introduces a novel approach, "3D Point-Cloud Processing Using Panoramic Images for Object Detection," aimed at enhancing the interpretability of laser point clouds through the integration of color information derived from panoramic images. Focusing on the context of Mobile Measurement Systems (MMS), where various digital cameras are utilized, the work addresses the challenges associated with processing panoramic images offering a 360-degree view angle. The core objective is to develop a

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Julin, Arttu, Matti Kurkela, Toni Rantanen, et al. "Evaluating the Quality of TLS Point Cloud Colorization." Remote Sensing 12, no. 17 (2020): 2748. http://dx.doi.org/10.3390/rs12172748.

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Terrestrial laser scanning (TLS) enables the efficient production of high-density colored 3D point clouds of real-world environments. An increasing number of applications from visual and automated interpretation to photorealistic 3D visualizations and experiences rely on accurate and reliable color information. However, insufficient attention has been put into evaluating the colorization quality of the 3D point clouds produced applying TLS. We have developed a method for the evaluation of the point cloud colorization quality of TLS systems with integrated imaging sensors. Our method assesses t

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Wang, Z., M. Varga, T. Medić, and A. Wieser. "ASSESSING THE ALIGNMENT BETWEEN GEOMETRY AND COLORS IN TLS COLORED POINT CLOUDS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (December 5, 2023): 597–604. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-597-2023.

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Abstract. The integration of the color information from RGB cameras with the point cloud geometry is used in numerous applications. However, little attention has been paid on errors that occur when aligning colors to points in terrestrial laser scanning (TLS) point clouds. Such errors may impact the performance of algorithms that utilize colored point clouds. Herein, we propose a procedure for assessing the alignment between the TLS point cloud geometry and colors. The procedure is based upon identifying artificial targets observed in both LiDAR-based point cloud intensity data and camera-base

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Peng, Cheng-Wei, Chen-Chien Hsu, and Wei-Yen Wang. "Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction." Sensors 20, no. 22 (2020): 6536. http://dx.doi.org/10.3390/s20226536.

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Survey-grade Lidar brands have commercialized Lidar-based mobile mapping systems (MMSs) for several years now. With this high-end equipment, the high-level accuracy quality of point clouds can be ensured, but unfortunately, their high cost has prevented practical implementation in autonomous driving from being affordable. As an attempt to solve this problem, we present a cost-effective MMS to generate an accurate 3D color point cloud for autonomous vehicles. Among the major processes for color point cloud reconstruction, we first synchronize the timestamps of each sensor. The calibration proce

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Wen, C., S. Lin, C. Wang, and J. Li. "Planar surface segmentation using a color-enhanced hybrid model for RGB-D camera-based indoor mobile mapping point clouds." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-1 (November 7, 2014): 61–67. http://dx.doi.org/10.5194/isprsannals-ii-1-61-2014.

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Point clouds acquired by RGB-D camera-based indoor mobile mapping system suffer the problems of being noisy, exhibiting an uneven distribution, and incompleteness, which are the problems that introduce difficulties for point cloud planar surface segmentation. This paper presents a novel color-enhanced hybrid planar surface segmentation model for RGB-D camera-based indoor mobile mapping point clouds based on region growing method, and the model specially addresses the planar surface extraction task over point cloud according to the noisy and incomplete indoor mobile mapping point clouds. The pr

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Zhou, H. M., Z. G. Liu, M. X. Li, and B. H. Lu. "A Fast Reconstruction of Dense Unorganized Point Cloud Based on 3-Color Octree." Materials Science Forum 628-629 (August 2009): 293–98. http://dx.doi.org/10.4028/www.scientific.net/msf.628-629.293.

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This paper describes a fast reconstruction algorithm of implicit model based on 3-color octree structure for dense unorganized point cloud. At first, the point cloud is stored with an extended octree, 3-color octree. Aiming at this 3-color octree structure a new node watershed algorithm is presented with a higher efficiency to estimate the signs of subdivided leaf nodes. So the leaf nodes are divided into three types: interior, boundary and exterior nodes. To quickly reconstruct the model we sample the 3-color octree structure only at boundary nodes, which greatly reduces the number of sampled

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Li, Peng, Ruisheng Wang, Yanxia Wang, and Ge Gao. "Fast Method of Registration for 3D RGB Point Cloud with Improved Four Initial Point Pairs Algorithm." Sensors 20, no. 1 (2019): 138. http://dx.doi.org/10.3390/s20010138.

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Three-dimensional (3D) point cloud registration is an important step in three-dimensional (3D) model reconstruction or 3D mapping. Currently, there are many methods for point cloud registration, but these methods are not able to simultaneously solve the problem of both efficiency and precision. We propose a fast method of global registration, which is based on RGB (Red, Green, Blue) value by using the four initial point pairs (FIPP) algorithm. First, the number of different RGB values of points in a dataset are counted and the colors in the target dataset having too few points are discarded by

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Teng, Xiaowen, Guangsheng Zhou, Yuxuan Wu, Chenglong Huang, Wanjing Dong, and Shengyong Xu. "Three-Dimensional Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera." Sensors 21, no. 14 (2021): 4628. http://dx.doi.org/10.3390/s21144628.

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The three-dimensional reconstruction method using RGB-D camera has a good balance in hardware cost and point cloud quality. However, due to the limitation of inherent structure and imaging principle, the acquired point cloud has problems such as a lot of noise and difficult registration. This paper proposes a 3D reconstruction method using Azure Kinect to solve these inherent problems. Shoot color images, depth images and near-infrared images of the target from six perspectives by Azure Kinect sensor with black background. Multiply the binarization result of the 8-bit infrared image with the R

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Et.al, Eunchong Ha. "Color Interactive Contents System using Kinect Camera Calibration." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 786–91. http://dx.doi.org/10.17762/turcomat.v12i6.2096.

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Recently, media content that interacts in real time is increasing. In this paper, we introduce a real-time color extraction content system that utilizes the Kinect camera used in ‘COLOR’ media art. The Kinect camera used in the work detects and tracks the joints of the visitors that enter the exhibition space. Kinect detected data is mapped to color calibration in a Unity environment to generate a point cloud video. Get the pixel color of the spine shoulder joint coordinates of the visitor in the point cloud image. The color data is output on the screen in the form of color one, and passes thr

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Choi, Ouk, Min-Gyu Park, and Youngbae Hwang. "Iterative K-Closest Point Algorithms for Colored Point Cloud Registration." Sensors 20, no. 18 (2020): 5331. http://dx.doi.org/10.3390/s20185331.

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We present two algorithms for aligning two colored point clouds. The two algorithms are designed to minimize a probabilistic cost based on the color-supported soft matching of points in a point cloud to their K-closest points in the other point cloud. The first algorithm, like prior iterative closest point algorithms, refines the pose parameters to minimize the cost. Assuming that the point clouds are obtained from RGB-depth images, our second algorithm regards the measured depth values as variables and minimizes the cost to obtain refined depth values. Experiments with our synthetic dataset s

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Liang, Dong, Zeyu Zhang, Qiang Zhang, Erpeng Wu, and Haibin Huang. "Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements." Structural Control and Health Monitoring 2024 (February 13, 2024): 1–19. http://dx.doi.org/10.1155/2024/9687805.

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Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple

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Bolkas, Dimitrios, and Aaron Martinez. "Effect of target color and scanning geometry on terrestrial LiDAR point-cloud noise and plane fitting." Journal of Applied Geodesy 12, no. 1 (2018): 109–27. http://dx.doi.org/10.1515/jag-2017-0034.

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AbstractPoint-cloud coordinate information derived from terrestrial Light Detection And Ranging (LiDAR) is important for several applications in surveying and civil engineering. Plane fitting and segmentation of target-surfaces is an important step in several applications such as in the monitoring of structures. Reliable parametric modeling and segmentation relies on the underlying quality of the point-cloud. Therefore, understanding how point-cloud errors affect fitting of planes and segmentation is important. Point-cloud intensity, which accompanies the point-cloud data, often goes hand-in-h

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Han, Tianyu, Ruijie Zhang, Jiangming Kan, Ruifang Dong, Xixuan Zhao, and Shun Yao. "A Point Cloud Registration Framework with Color Information Integration." Remote Sensing 16, no. 5 (2024): 743. http://dx.doi.org/10.3390/rs16050743.

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Point cloud registration serves as a critical tool for constructing 3D environmental maps. Both geometric and color information are instrumental in differentiating diverse point features. Specifically, when points appear similar based solely on geometric features, rendering them challenging to distinguish, the color information embedded in the point cloud carries significantly important features. In this study, the colored point cloud is utilized in the FCGCF algorithm, a refined version of the FCGF algorithm, incorporating color information. Moreover, we introduce the PointDSCC method, which

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Li, Yong, Yinzheng Luo, Xia Gu, Dong Chen, Fang Gao, and Feng Shuang. "Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels." Remote Sensing 13, no. 16 (2021): 3156. http://dx.doi.org/10.3390/rs13163156.

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Point cloud classification is a key technology for point cloud applications and point cloud feature extraction is a key step towards achieving point cloud classification. Although there are many point cloud feature extraction and classification methods, and the acquisition of colored point cloud data has become easier in recent years, most point cloud processing algorithms do not consider the color information associated with the point cloud or do not make full use of the color information. Therefore, we propose a voxel-based local feature descriptor according to the voxel-based local binary p

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Rai, A., N. Srivastava, K. Khoshelham, and K. Jain. "SEMANTIC ENRICHMENT OF 3D POINT CLOUDS USING 2D IMAGE SEGMENTATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 14, 2023): 1659–66. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1659-2023.

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Abstract. 3D point cloud segmentation is computationally intensive due to the lack of inherent structural information and the unstructured nature of the point cloud data, which hinders the identification and connection of neighboring points. Understanding the structure of the point cloud data plays a crucial role in obtaining a meaningful and accurate representation of the underlying 3D environment. In this paper, we propose an algorithm that builds on existing state-of-the-art techniques of 2D image segmentation and point cloud registration to enrich point clouds with semantic information. De

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Kim, S., H. G. Kim, and T. Kim. "MESH MODELLING OF 3D POINT CLOUD FROM UAV IMAGES BY POINT CLASSIFICATION AND GEOMETRIC CONSTRAINTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 507–11. http://dx.doi.org/10.5194/isprs-archives-xlii-2-507-2018.

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The point cloud generated by multiple image matching is classified as an unstructured point cloud because it is not regularly point spaced and has multiple viewpoints. The surface reconstruction technique is used to generate mesh model using unstructured point clouds. In the surface reconstruction process, it is important to calculate correct surface normals. The point cloud extracted from multi images contains position and color information of point as well as geometric information of images used in the step of point cloud generation. Thus, the surface normal estimation based on the geometric

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Li, Quan, and Xiaojun Cheng. "Comparison of Different Feature Sets for TLS Point Cloud Classification." Sensors 18, no. 12 (2018): 4206. http://dx.doi.org/10.3390/s18124206.

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Point cloud classification is an essential requirement for effectively utilizing point cloud data acquired by Terrestrial laser scanning (TLS). Neighborhood selection, feature selection and extraction, and classification of points based on the respective features constitute the commonly used workflow of point cloud classification. Feature selection and extraction has been the focus of many studies, and the choice of different features has had a great impact on classification results. In previous studies, geometric features were widely used for TLS point cloud classification, and only a few stu

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Wang, Yuhao, Yong Zuo, Zhihua Du, et al. "MInet: A Novel Network Model for Point Cloud Processing by Integrating Multi-Modal Information." Sensors 23, no. 14 (2023): 6327. http://dx.doi.org/10.3390/s23146327.

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Three-dimensional LiDAR systems that capture point cloud data enable the simultaneous acquisition of spatial geometry and multi-wavelength intensity information, thereby paving the way for three-dimensional point cloud recognition and processing. However, due to the irregular distribution, low resolution of point clouds, and limited spatial recognition accuracy in complex environments, inherent errors occur in classifying and segmenting the acquired target information. Conversely, two-dimensional visible light images provide real-color information, enabling the distinction of object contours a

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Sa, Guodong, Yipeng Chao, Shuo Li, Dandan Liu, and Zonghua Wang. "A Globally Consistent Merging Method for House Point Clouds Based on Artificially Enhanced Features." Electronics 13, no. 16 (2024): 3179. http://dx.doi.org/10.3390/electronics13163179.

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In the process of using structured light technology to obtain indoor point clouds, due to the limited field of view of the device, it is necessary to obtain multiple point clouds of different wall surfaces. Therefore, merging the point cloud is necessary to acquire a complete point cloud. However, due to issues such as the sparse geometric features of the wall point clouds and the high similarity of multiple point clouds, the merging effect of point clouds is poor. In this paper, we leverage artificially enhanced features to improve the accuracy of registration in this scenario. Firstly, we de

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Youssefi, David, Dawa Derksen, Damien Migel-Arachchige, Jasmin Siefert, Loïc Dumas, and Jonathan Guinet. "Geometrically guided and confidence-based point cloud denoising." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W12-2024 (June 20, 2024): 149–55. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w12-2024-149-2024.

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Abstract. The generation of photogrammetric point clouds from satellite images is often based on image correlation techniques. Correlation errors can arise for a wide variety of reasons: transient objects, homogeneous areas, shadows, and surface discontinuities. Therefore, a simple 3D Gaussian distribution at the point cloud level is not an appropriate model. In this paper, we propose a new point cloud denoising method integrated into the Multiview Stereo Pipeline CARS, dedicated to satellite imagery. Building upon bilateral filtering principles, our approach introduces a novel utilization of

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Su, Jingxin, Ryuji Miyazaki, Toru Tamaki, and Kazufumi Kaneda. "3D Modeling of Lane Marks Using a Combination of Images and Mobile Mapping Data." International Journal of Automation Technology 12, no. 3 (2018): 386–94. http://dx.doi.org/10.20965/ijat.2018.p0386.

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When we drive a car, the white lines on the road show us where the lanes are. The lane marks act as a reference for where to steer the vehicle. Naturally, in the field of advanced driver-assistance systems and autonomous driving, lane-line detection has become a critical issue. In this research, we propose a fast and precise method that can create a three-dimensional point cloud model of lane marks. Our datasets are obtained by a vehicle-mounted mobile mapping system (MMS). The input datasets include point cloud data and color images generated by laser scanner and CCD camera. A line-based poin

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Jiang, Haiyang, Yuanyao Lu, and Shengnan Chen. "Research on 3D Point Cloud Object Detection Algorithm for Autonomous Driving." Mathematical Problems in Engineering 2022 (February 17, 2022): 1–13. http://dx.doi.org/10.1155/2022/8151805.

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In autonomous driving, lidar has become the main vehicle sensor due to its advantages such as long-range measurement and high accuracy. However, the collected point cloud data is sparse and unevenly distributed, and it lacks characterization capabilities when facing objects with missing or similar shapes, so that the detection accuracy is low while detecting long-distance small targets with similar shapes and a small number of point clouds. In order to improve the detection accuracy of small targets represented by point clouds, this paper adopts a method that fuses point cloud and RGB image to

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Men, Hao, and Kishore Pochiraju. "Hue-assisted automatic registration of color point clouds." Journal of Computational Design and Engineering 1, no. 4 (2014): 223–32. http://dx.doi.org/10.7315/jcde.2014.022.

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Abstract This paper describes a variant of the extended Gaussian image based registration algorithm for point clouds with surface color information. The method correlates the distributions of surface normals for rotational alignment and grid occupancy for translational alignment with hue filters applied during the construction of surface normal histograms and occupancy grids. In this method, the size of the point cloud is reduced with a hue-based down sampling that is independent of the point sample density or local geometry. Experimental results show that use of the hue filters increases the

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Pérez, Emiliano, Pilar Merchán, Alejandro Espacio, and Santiago Salamanca. "Fusion of Thermal Point Cloud Series of Buildings for Inspection in Virtual Reality." Buildings 14, no. 7 (2024): 2127. http://dx.doi.org/10.3390/buildings14072127.

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Point cloud acquisition systems now enable the capture of geometric models enriched with additional attribute data, providing a deeper semantic understanding of the measured environments. However, visualizing complex spatiotemporal point clouds remains computationally challenging. This paper presents a fusion methodology that aggregates points from different instants into unified clouds with reduced redundancy while preserving time-varying information. The static 3D structure is condensed using a voxel approach, while temporal attributes are propagated across the merged data. The resulting poi

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Peng, Zhao, Li Yue, and Ning Xiao. "Simultaneous Wood Defect and Species Detection with 3D Laser Scanning Scheme." International Journal of Optics 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/7049523.

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Wood grading and wood price are mainly connected with the wood defect and wood species. In this paper, a wood defect quantitative detection scheme and a wood species qualitative identification scheme are proposed simultaneously based on 3D laser scanning point cloud. First, an Artec 3D scanner is used to scan the wood surface to get the 3D point cloud. Each 3D point contains its X, Y, and Z coordinate and its RGB color information. After preprocessing, the Z coordinate value of current point is compared with the set threshold to judge whether it is a defect point (i.e., cavity, worm tunnel, an

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Deng, Haokun, Hongyang Zhu, and Jianping Guo. "Research on Deformation Monitoring of Roadway Surrounding Rock Based on Mobile 3D Laser Scanning Technology." Academic Journal of Science and Technology 10, no. 3 (2024): 155–62. http://dx.doi.org/10.54097/8xfqgr47.

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Aiming at the problems of low data acquisition efficiency, unintuitive data form, low relative accuracy, and inability to continuously monitor the whole cross-section in traditional roadway rock deformation monitoring methods, a roadway rock deformation monitoring method based on mobile 3D laser scanning technology is proposed. Firstly, the mobile 3D laser scanning technology is used to obtain the real 3D point cloud data of the roadway surrounding rock; secondly, the multi-scale dimensional features are used to classify the 3D point cloud data, filter the invalid point cloud data, and form th

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Yu, Jiayong, Longchen Ma, Maoyi Tian, and Xiushan Lu. "Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds." Journal of Imaging Science and Technology 65, no. 1 (2021): 10501–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2021.65.1.010501.

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Abstract The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering

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Rogachev, Dmitriy, Ivan Kozlov, and Vladislav Klubnichkin. "Noise filtering of the forest site scanned by LiDAR based on YCbCr and L*a*b* color models." Forestry Engineering Journal 13, no. 4 (2024): 125–39. http://dx.doi.org/10.34220/issn.2222-7962/2023.4/8.

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Point clouds are widely used in ground-based forest scanning using LiDAR and stereo cameras. Point clouds often suffer from noise outliers and artifacts that distort data. Hardware accuracy and quality of the initial point cloud during ground scanning of a forest area can be improved by using scanners with higher expansion, as well as using photogrammetry or additional sensors. To eliminate noise, software methods can be used: point filtering, smoothing, statistical methods and reconstruction algorithms. A new approach to filtering the noise of the scanned forest area i

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Yang, Jing, Haozhe Li, Zhou Jiang, Dong Zhang, Xiaoli Yue, and Shaoyi Du. "Color guided convolutional network for point cloud semantic segmentation." International Journal of Advanced Robotic Systems 19, no. 3 (2022): 172988062210985. http://dx.doi.org/10.1177/17298806221098506.

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Point cloud semantic segmentation based on deep learning methods is still a challenge due to the irregularity of structures and uncertainty of sampling. Color information often contains a lot of prior information, whereas the existing methods do not attach more importance to it. To deal with this problem, we propose a novel hard attention mechanism, named color-guided convolution. This convolution operator learns the correlation between geometric and color information by reordering the local points with color-indicated vectors. In addition, the global feature fusion is proposed to rectify feat

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Qiu, Long, Huaibin Zheng, Yuyuan Han, et al. "Fusion of near-infrared single-photon LiDAR with visible light camera." Measurement Science and Technology 36, no. 6 (2025): 065409. https://doi.org/10.1088/1361-6501/addddf.

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Abstract The fusion of light detection and ranging (LiDAR) and camera provides comprehensive information about the depth and color texture of objects, finding wide applications in autonomous driving, three-dimensional (3D) scene reconstruction, and other fields. However, point clouds acquired via LiDAR are susceptible to noise, impacting downstream tasks like surface reconstruction and object recognition demanding high-quality point cloud outputs from 3D imaging systems. This paper proposes a novel 3D fusion imaging technique that enhances the quality of the fused point clouds from three disti

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Guo, Jiateng, Zirui Zhang, Yachun Mao, Shanjun Liu, Wancheng Zhu, and Tianhong Yang. "Automatic Extraction of Discontinuity Traces from 3D Rock Mass Point Clouds Considering the Influence of Light Shadows and Color Change." Remote Sensing 14, no. 21 (2022): 5314. http://dx.doi.org/10.3390/rs14215314.

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The spatial characteristics of discontinuity traces play an important role in evaluations of the quality of rock masses. Most researchers have extracted discontinuity traces through the gray attributes of two-dimensional (2D) photo images or the geometric attributes of three-dimensional (3D) point clouds, while few researchers have paid attention to other important attributes of the original 3D point clouds, that is, the color attributes. By analyzing the color changes in a 3D point cloud, discontinuity traces in the smooth areas of a rock surface can be extracted, which cannot be obtained fro

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Gao, Hongjuan, Hui Wang, and Shijie Zhao. "A Color- and Geometric-Feature-Based Approach for Denoising Three-Dimensional Cultural Relic Point Clouds." Entropy 26, no. 4 (2024): 319. http://dx.doi.org/10.3390/e26040319.

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In the acquisition process of 3D cultural relics, it is common to encounter noise. To facilitate the generation of high-quality 3D models, we propose an approach based on graph signal processing that combines color and geometric features to denoise the point cloud. We divide the 3D point cloud into patches based on self-similarity theory and create an appropriate underlying graph with a Markov property. The features of the vertices in the graph are represented using 3D coordinates, normal vectors, and color. We formulate the point cloud denoising problem as a maximum a posteriori (MAP) estimat

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Kang, Jitong, Ning Chen, Mei Li, et al. "A Point Cloud Segmentation Method for Dim and Cluttered Underground Tunnel Scenes Based on the Segment Anything Model." Remote Sensing 16, no. 1 (2023): 97. http://dx.doi.org/10.3390/rs16010097.

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In recent years, point cloud segmentation technology has increasingly played a pivotal role in tunnel construction and maintenance. Currently, traditional methods for segmenting point clouds in tunnel scenes often rely on a multitude of attribute information, including spatial distribution, color, normal vectors, intensity, and density. However, the underground tunnel scenes show greater complexity than road tunnel scenes, such as dim light, indistinct boundaries of tunnel walls, and disordered pipelines. Furthermore, issues pertaining to data quality, such as the lack of color information and

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Yang, Yang, Weile Chen, Muyi Wang, Dexing Zhong, and Shaoyi Du. "Color Point Cloud Registration Based on Supervoxel Correspondence." IEEE Access 8 (2020): 7362–72. http://dx.doi.org/10.1109/access.2020.2963987.

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Zhang, Ximin, Wanggen Wan, and Xuandong An. "Clustering and DCT Based Color Point Cloud Compression." Journal of Signal Processing Systems 86, no. 1 (2015): 41–49. http://dx.doi.org/10.1007/s11265-015-1095-0.

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Zhang, X. D., Chang Ku Sun, C. Wang, and S. H. Ye. "Study on Preprocessing Methods for Color 3D Point Cloud." Materials Science Forum 471-472 (December 2004): 716–21. http://dx.doi.org/10.4028/www.scientific.net/msf.471-472.716.

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This paper provides a preprocessing flow for color three-dimension (3D) point cloud according to the characteristics of laser scanning data. The preprocessing methods and their functions are introduced in detail. Automatic system decision and manual polygon selection methods are applied to eliminate unwanted and noise data successfully, which possibly make improper color models reconstructed. A data reduction method is presented based on Grid reduction method considering color-boundary preservation. It can effectively avoid shape and color distortion in model reconstruction. Several experiment

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Wang, Zongshun. "Large-scale Point Cloud Segmentation based on Multi-feature Local Enhanced Fusion." International Journal of Computer Science and Information Technology 2, no. 1 (2024): 162–73. http://dx.doi.org/10.62051/ijcsit.v2n1.19.

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This paper introduces a framework for large-scale 3D point cloud semantic segmentation - the MLEF-Net model. The model aims to improve the segmentation accuracy of large-scale point clouds by innovatively combining Manhattan distance-based KNN neighborhood search with feature aggregation techniques. This approach uniquely handles spatial, color, and normal vector attributes, thereby improving the segmentation results. The superiority of the model is validated through comprehensive testing on the SemanticKITTI and nuScenes datasets, demonstrating its potential to enhance point cloud segmentatio

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Widyaningrum, E., M. K. Fajari, R. C. Lindenbergh, and M. Hahn. "TAILORED FEATURES FOR SEMANTIC SEGMENTATION WITH A DGCNN USING FREE TRAINING SAMPLES OF A COLORED AIRBORNE POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 339–46. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-339-2020.

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Abstract. Automation of 3D LiDAR point cloud processing is expected to increase the production rate of many applications including automatic map generation. Fast development on high-end hardware has boosted the expansion of deep learning research for 3D classification and segmentation. However, deep learning requires large amount of high quality training samples. The generation of training samples for accurate classification results, especially for airborne point cloud data, is still problematic. Moreover, which customized features should be used best for segmenting airborne point cloud data i

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Chen, Jingdao, John Seon Keun Yi, Mark Kahoush, Erin S. Cho, and Yong K. Cho. "Point Cloud Scene Completion of Obstructed Building Facades with Generative Adversarial Inpainting." Sensors 20, no. 18 (2020): 5029. http://dx.doi.org/10.3390/s20185029.

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Collecting 3D point cloud data of buildings is important for many applications such as urban mapping, renovation, preservation, and energy simulation. However, laser-scanned point clouds are often difficult to analyze, visualize, and interpret due to incompletely scanned building facades caused by numerous sources of defects such as noise, occlusions, and moving objects. Several point cloud scene completion algorithms have been proposed in the literature, but they have been mostly applied to individual objects or small-scale indoor environments and not on large-scale scans of building facades.

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Shao, J., W. Zhang, Y. Zhu, and A. Shen. "FAST REGISTRATION OF TERRESTRIAL LIDAR POINT CLOUD AND SEQUENCE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 875–79. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-875-2017.

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Image has rich color information, and it can help to promote recognition and classification of point cloud. The registration is an important step in the application of image and point cloud. In order to give the rich texture and color information for LiDAR point cloud, the paper researched a fast registration method of point cloud and sequence images based on the ground-based LiDAR system. First, calculating transformation matrix of one of sequence images based on 2D image and LiDAR point cloud; second, using the relationships of position and attitude information among multi-angle sequence ima

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Jiang, Yong. "Based on Virtual Reality Technology Research on Innovation and Design of Ceramic Painting Products." Mathematical Problems in Engineering 2022 (July 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/4421769.

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In order to obtain the complete color point cloud data of traditional ceramic artworks and reconstruct them efficiently, a 3D digital reconstruction method of ceramic painting products based on virtual reality technology was proposed. By the method, the 3D color point cloud data of ceramic works were collected by the camera 3D scanning equipment. The new postprocessing methods such as fusion and repair were investigated and proposed so as to obtain the complete optimized color point cloud information. The high-fidelity 3D mesh model and simplified mesh of ceramic artworks were obtained through

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Freitas, Pedro Garcia, Rafael Diniz, and Mylene Christine Queiroz De Farias. "Assessing the quality of 3D point clouds using descriptors for color and geometry texture." Brazilian Journal of Development 9, no. 05 (2023): 17415–31. http://dx.doi.org/10.34117/bjdv9n5-196.

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Since the mid-20th century, the use of digital formats for visual content has revolutionized communication in society. The Internet and digital broadcasting systems, which became widely available in the 1990s, led to an incredible expansion of multimedia consumption among the public. As a result, telecommunication networks and providers were pushed to their limits to meet the growing demand for multimedia content. Traditional electronic imaging systems, such as TV broadcasting systems, were designed based on subjective quality analysis for defining parameters like the number of lines in a vide

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Meng, Yuan, Xibin Dong, Kaili Han, Hui Liu, Hangfeng Qu, and Tong Gao. "Classification of Tree Species Using Point Cloud Features from Terrestrial Laser Scanning." Forests 15, no. 12 (2024): 2110. http://dx.doi.org/10.3390/f15122110.

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The automatic classification of tree species using terrestrial laser scanning (TLS) point clouds is key in forestry research. This study aims to develop a robust framework for tree species classification by integrating advanced feature extraction and machine learning techniques. Such a framework is of great significance for investigating and monitoring forest resources, sustainable forest management, and biodiversity research. To achieve this, point cloud data from 360 trees of four species were collected at the Northeastern Forestry University in Harbin City, Heilongjiang Province. Three type

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Guo, Jingwei, and Lihong Xu. "Automatic Segmentation for Plant Leaves via Multiview Stereo Reconstruction." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/9845815.

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This paper presented a new method for automatic plant point cloud acquisition and leaves segmentation. Quasi-dense point cloud of the plant is obtained from multiview stereo reconstruction based on surface expansion. In order to overcome the negative effects from complex natural light changes and to obtain a more accurate plant point cloud, the Adaptive Normalized Cross-Correlation algorithm is used in calculating the matching cost between two images, which is robust to radiometric factors and can reduce the fattening effect around boundaries. In the stage of segmentation for each single leaf,

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Pepe, M., S. Ackermann, L. Fregonese, and C. Achille. "NEW PERSPECTIVES OF POINT CLOUDS COLOR MANAGEMENT – THE DEVELOPMENT OF TOOL IN MATLAB FOR APPLICATIONS IN CULTURAL HERITAGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W3 (February 23, 2017): 567–71. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w3-567-2017.

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The paper describes a method for Point Clouds Color management and Integration obtained from Terrestrial Laser Scanner (TLS) and Image Based (IB) survey techniques. Especially in the Cultural Heritage (CH) environment, methods and techniques to improve the color quality of Point Clouds have a key role because a homogenous texture brings to a more accurate reconstruction of the investigated object and to a more pleasant perception of the color object as well. A color management method for point clouds can be useful in case of single data set acquired by TLS or IB technique as well as in case of

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