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Relevant bibliographies by topics / 3D Point cloud Compression

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Academic literature on the topic '3D Point cloud Compression'

Author: Grafiati

Published: 11 March 2023

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Journal articles on the topic "3D Point cloud Compression"

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Huang, Tianxin, Jiangning Zhang, Jun Chen, et al. "3QNet." ACM Transactions on Graphics 41, no. 6 (2022): 1–13. http://dx.doi.org/10.1145/3550454.3555481.

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Since the development of 3D applications, the point cloud, as a spatial description easily acquired by sensors, has been widely used in multiple areas such as SLAM and 3D reconstruction. Point Cloud Compression (PCC) has also attracted more attention as a primary step before point cloud transferring and saving, where the geometry compression is an important component of PCC to compress the points geometrical structures. However, existing non-learning-based geometry compression methods are often limited by manually pre-defined compression rules. Though learning-based compression methods can sig

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Morell, Vicente, Sergio Orts, Miguel Cazorla, and Jose Garcia-Rodriguez. "Geometric 3D point cloud compression." Pattern Recognition Letters 50 (December 2014): 55–62. http://dx.doi.org/10.1016/j.patrec.2014.05.016.

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Yu, Siyang, Si Sun, Wei Yan, Guangshuai Liu, and Xurui Li. "A Method Based on Curvature and Hierarchical Strategy for Dynamic Point Cloud Compression in Augmented and Virtual Reality System." Sensors 22, no. 3 (2022): 1262. http://dx.doi.org/10.3390/s22031262.

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As a kind of information-intensive 3D representation, point cloud rapidly develops in immersive applications, which has also sparked new attention in point cloud compression. The most popular dynamic methods ignore the characteristics of point clouds and use an exhaustive neighborhood search, which seriously impacts the encoder’s runtime. Therefore, we propose an improved compression means for dynamic point cloud based on curvature estimation and hierarchical strategy to meet the demands in real-world scenarios. This method includes initial segmentation derived from the similarity between norm

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Imdad, Ulfat, Mirza Tahir Ahmed, Muhammad Asif, and Hanan Aljuaid. "3D point cloud lossy compression using quadric surfaces." PeerJ Computer Science 7 (October 6, 2021): e675. http://dx.doi.org/10.7717/peerj-cs.675.

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The presence of 3D sensors in hand-held or head-mounted smart devices has motivated many researchers around the globe to devise algorithms to manage 3D point cloud data efficiently and economically. This paper presents a novel lossy compression technique to compress and decompress 3D point cloud data that will save storage space on smart devices as well as minimize the use of bandwidth when transferred over the network. The idea presented in this research exploits geometric information of the scene by using quadric surface representation of the point cloud. A region of a point cloud can be rep

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Yu, Jiawen, Jin Wang, Longhua Sun, Mu-En Wu, and Qing Zhu. "Point Cloud Geometry Compression Based on Multi-Layer Residual Structure." Entropy 24, no. 11 (2022): 1677. http://dx.doi.org/10.3390/e24111677.

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Point cloud data are extensively used in various applications, such as autonomous driving and augmented reality since it can provide both detailed and realistic depictions of 3D scenes or objects. Meanwhile, 3D point clouds generally occupy a large amount of storage space that is a big burden for efficient communication. However, it is difficult to efficiently compress such sparse, disordered, non-uniform and high dimensional data. Therefore, this work proposes a novel deep-learning framework for point cloud geometric compression based on an autoencoder architecture. Specifically, a multi-laye

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Quach, Maurice, Aladine Chetouani, Giuseppe Valenzise, and Frederic Dufaux. "A deep perceptual metric for 3D point clouds." Electronic Imaging 2021, no. 9 (2021): 257–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-257.

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Point clouds are essential for storage and transmission of 3D content. As they can entail significant volumes of data, point cloud compression is crucial for practical usage. Recently, point cloud geometry compression approaches based on deep neural networks have been explored. In this paper, we evaluate the ability to predict perceptual quality of typical voxel-based loss functions employed to train these networks. We find that the commonly used focal loss and weighted binary cross entropy are poorly correlated with human perception. We thus propose a perceptual loss function for 3D point clo

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Lee, Mun-yong, Sang-ha Lee, Kye-dong Jung, Seung-hyun Lee, and Soon-chul Kwon. "A Novel Preprocessing Method for Dynamic Point-Cloud Compression." Applied Sciences 11, no. 13 (2021): 5941. http://dx.doi.org/10.3390/app11135941.

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Computer-based data processing capabilities have evolved to handle a lot of information. As such, the complexity of three-dimensional (3D) models (e.g., animations or real-time voxels) containing large volumes of information has increased exponentially. This rapid increase in complexity has led to problems with recording and transmission. In this study, we propose a method of efficiently managing and compressing animation information stored in the 3D point-clouds sequence. A compressed point-cloud is created by reconfiguring the points based on their voxels. Compared with the original point-cl

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Luo, Guoliang, Bingqin He, Yanbo Xiong, et al. "An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression." Sensors 23, no. 4 (2023): 2250. http://dx.doi.org/10.3390/s23042250.

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Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compress

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Gu, Shuai, Junhui Hou, Huanqiang Zeng, and Hui Yuan. "3D Point Cloud Attribute Compression via Graph Prediction." IEEE Signal Processing Letters 27 (2020): 176–80. http://dx.doi.org/10.1109/lsp.2019.2963793.

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Dybedal, Joacim, Atle Aalerud, and Geir Hovland. "Embedded Processing and Compression of 3D Sensor Data for Large Scale Industrial Environments." Sensors 19, no. 3 (2019): 636. http://dx.doi.org/10.3390/s19030636.

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This paper presents a scalable embedded solution for processing and transferring 3D point cloud data. Sensors based on the time-of-flight principle generate data which are processed on a local embedded computer and compressed using an octree-based scheme. The compressed data is transferred to a central node where the individual point clouds from several nodes are decompressed and filtered based on a novel method for generating intensity values for sensors which do not natively produce such a value. The paper presents experimental results from a relatively large industrial robot cell with an ap

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Dissertations / Theses on the topic "3D Point cloud Compression"

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Morell, Vicente. "Contributions to 3D Data Registration and Representation." Doctoral thesis, Universidad de Alicante, 2014. http://hdl.handle.net/10045/42364.

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Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same informati

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Roure, Garcia Ferran. "Tools for 3D point cloud registration." Doctoral thesis, Universitat de Girona, 2017. http://hdl.handle.net/10803/403345.

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In this thesis, we did an in-depth review of the state of the art of 3D registration, evaluating the most popular methods. Given the lack of standardization in the literature, we also proposed a nomenclature and a classification to unify the evaluation systems and to be able to compare the different algorithms under the same criteria. The major contribution of the thesis is the Registration Toolbox, which consists of software and a database of 3D models. The software presented here consists of a 3D Registration Pipeline written in C ++ that allows researchers to try different methods, as we

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Tarcin, Serkan. "Fast Feature Extraction From 3d Point Cloud." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615659/index.pdf.

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To teleoperate an unmanned vehicle a rich set of information should be gathered from surroundings.These systems use sensors which sends high amounts of data and processing the data in CPUs can be time consuming. Similarly, the algorithms that use the data may work slow because of the amount of the data. The solution is, preprocessing the data taken from the sensors on the vehicle and transmitting only the necessary parts or the results of the preprocessing. In this thesis a 180 degree laser scanner at the front end of an unmanned ground vehicle (UGV) tilted up and down on a horizontal axis and

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Forsman, Mona. "Point cloud densification." Thesis, Umeå universitet, Institutionen för fysik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-39980.

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Several automatic methods exist for creating 3D point clouds extracted from 2D photos. In manycases, the result is a sparse point cloud, unevenly distributed over the scene.After determining the coordinates of the same point in two images of an object, the 3D positionof that point can be calculated using knowledge of camera data and relative orientation. A model created from a unevenly distributed point clouds may loss detail and precision in thesparse areas. The aim of this thesis is to study methods for densification of point clouds. This thesis contains a literature study over different met

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Gujar, Sanket. "Pointwise and Instance Segmentation for 3D Point Cloud." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1290.

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The camera is the cheapest and computationally real-time option for detecting or segmenting the environment for an autonomous vehicle, but it does not provide the depth information and is undoubtedly not reliable during the night, bad weather, and tunnel flash outs. The risk of an accident gets higher for autonomous cars when driven by a camera in such situations. The industry has been relying on LiDAR for the past decade to solve this problem and focus on depth information of the environment, but LiDAR also has its shortcoming. The industry methods commonly use projections methods to create

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Chen, Chen. "Semantics Augmented Point Cloud Sampling for 3D Object Detection." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26956.

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3D object detection is an emerging topic among both industries and research communities. It aims at discovering objects of interest from 3D scenes and has a strong connection with many real-world scenarios, such as autonomous driving. Currently, many models have been proposed to detect potential objects from point clouds. Some methods attempt to model point clouds in the unit of point, and then perform detection with acquired point-wise features. These methods are classified as point-based methods. However, we argue that the prevalent sampling algorithm for point-based models is sub-optimal f

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Dey, Emon Kumar. "Effective 3D Building Extraction from Aerial Point Cloud Data." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/413311.

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Building extraction is important for a wider range of applications including smart city planning, disaster management, security, and cadastral mapping. This thesis mainly aims to present an effective data-driven strategy for building extraction using aerial Light Detection And Ranging (LiDAR) point cloud data. The LiDAR data provides highly accurate three-dimensional (3D) positional information. Therefore, studies on building extraction using LiDAR data have broadened in scope over time. Outliers, inharmonious input data behaviour, innumerable building structure possibilities, and heterogeneou

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Eckart, Benjamin. "Compact Generative Models of Point Cloud Data for 3D Perception." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1089.

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One of the most fundamental tasks for any robotics application is the ability to adequately assimilate and respond to incoming sensor data. In the case of 3D range sensing, modern-day sensors generate massive quantities of point cloud data that strain available computational resources. Dealing with large quantities of unevenly sampled 3D point data is a great challenge for many fields, including autonomous driving, 3D manipulation, augmented reality, and medical imaging. This thesis explores how carefully designed statistical models for point cloud data can facilitate, accelerate, and unify ma

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Oropallo, William Edward Jr. "A Point Cloud Approach to Object Slicing for 3D Printing." Thesis, University of South Florida, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10751757.

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<p> Various industries have embraced 3D printing for manufacturing on-demand, custom printed parts. However, 3D printing requires intelligent data processing and algorithms to go from CAD model to machine instructions. One of the most crucial steps in the process is the slicing of the object. Most 3D printers build parts by accumulating material layers by layer. 3D printing software needs to calculate these layers for manufacturing by slicing a model and calculating the intersections. Finding exact solutions of intersections on the original model is mathematically complicated and computational

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Lev, Hoang Justin. "A Study of 3D Point Cloud Features for Shape Retrieval." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM040.

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Grâce à l’amélioration et la multiplication des capteurs 3D, la diminution des prix et l’augmentation des puissances de calculs, l’utilisation de donnée3D s’est intensifiée ces dernières années. Les nuages de points 3D (3D pointcloud) sont une des représentations possibles pour de telles données. Elleà l’avantage d’être simple et précise, ainsi que le résultat immédiat de la capture. En tant que structure non-régulière sous forme de liste de points,l’analyse des nuages de points est complexe d’où leur récente utilisation. Cette thèse se concentre sur l’utilisation de nuages de points 3D pourun

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Books on the topic "3D Point cloud Compression"

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Liu, Shan, Min Zhang, Pranav Kadam, and C. C. Jay Kuo. 3D Point Cloud Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89180-0.

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Zhang, Guoxiang, and YangQuan Chen. Towards Optimal Point Cloud Processing for 3D Reconstruction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96110-7.

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Chen, YangQuan, and Guoxiang Zhang. Towards Optimal Point Cloud Processing for 3D Reconstruction. Springer International Publishing AG, 2022.

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3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods. Springer International Publishing AG, 2021.

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3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods. Springer International Publishing AG, 2022.

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Book chapters on the topic "3D Point cloud Compression"

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Tu, Chenxi. "Point Cloud Compression for 3D LiDAR Sensor." In Frontiers of Digital Transformation. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1358-9_8.

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Cheng, Shyi-Chyi, Ting-Lan Lin, and Ping-Yuan Tseng. "K-SVD Based Point Cloud Coding for RGB-D Video Compression Using 3D Super-Point Clustering." In MultiMedia Modeling. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37731-1_56.

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Alexandrov, Victor V., Sergey V. Kuleshov, Alexey J. Aksenov, and Alexandra A. Zaytseva. "The Method of Lossless 3D Point Cloud Compression Based on Space Filling Curve Implementation." In Automation Control Theory Perspectives in Intelligent Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33389-2_39.

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Héno, Raphaële, and Laure Chandelier. "Point Cloud Processing." In 3D Modeling of Buildings. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118648889.ch5.

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Weinmann, Martin. "Point Cloud Registration." In Reconstruction and Analysis of 3D Scenes. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29246-5_4.

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Liu, Shan, Min Zhang, Pranav Kadam, and C. C. Jay Kuo. "Deep Learning-Based Point Cloud Analysis." In 3D Point Cloud Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89180-0_3.

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Liu, Shan, Min Zhang, Pranav Kadam, and C. C. Jay Kuo. "Introduction." In 3D Point Cloud Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89180-0_1.

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Liu, Shan, Min Zhang, Pranav Kadam, and C. C. Jay Kuo. "Conclusion and Future Work." In 3D Point Cloud Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89180-0_5.

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Liu, Shan, Min Zhang, Pranav Kadam, and C. C. Jay Kuo. "Explainable Machine Learning Methods for Point Cloud Analysis." In 3D Point Cloud Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89180-0_4.

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McInerney, Daniel, and Pieter Kempeneers. "3D Point Cloud Data Processing." In Open Source Geospatial Tools. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01824-9_15.

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Conference papers on the topic "3D Point cloud Compression"

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Cao, Chao, Marius Preda, and Titus Zaharia. "3D Point Cloud Compression." In Web3D '19: The 24th International Conference on 3D Web Technology. ACM, 2019. http://dx.doi.org/10.1145/3329714.3338130.

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Renault, Sylvain, Thomas Ebner, Ingo Feldmann, and Oliver Schreer. "Point cloud compression framework for the web." In 2016 International Conference on 3D Imaging (IC3D). IEEE, 2016. http://dx.doi.org/10.1109/ic3d.2016.7823455.

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Huang, Tianxin, and Yong Liu. "3D Point Cloud Geometry Compression on Deep Learning." In MM '19: The 27th ACM International Conference on Multimedia. ACM, 2019. http://dx.doi.org/10.1145/3343031.3351061.

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Bui, Mai, Lin-Ching Chang, Hang Liu, Qi Zhao, and Genshe Chen. "Comparative Study of 3D Point Cloud Compression Methods." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671822.

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Xu, Jiacheng, Zhijun Fang, Yongbin Gao, et al. "Point AE-DCGAN: A deep learning model for 3D point cloud lossy geometry compression." In 2021 Data Compression Conference (DCC). IEEE, 2021. http://dx.doi.org/10.1109/dcc50243.2021.00085.

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Daribo, Ismael, Ryo Furukawa, Ryusuke Sagawa, and Hiroshi Kawasaki. "Adaptive arithmetic coding for point cloud compression." In 2012 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON 2012). IEEE, 2012. http://dx.doi.org/10.1109/3dtv.2012.6365475.

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Fan, Tingyu, Linyao Gao, Yiling Xu, Zhu Li, and Dong Wang. "D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/126.

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The non-uniformly distributed nature of the 3D Dynamic Point Cloud (DPC) brings significant challenges to its high-efficient inter-frame compression. This paper proposes a novel 3D sparse convolution-based Deep Dynamic Point Cloud Compression (D-DPCC) network to compensate and compress the DPC geometry with 3D motion estimation and motion compensation in the feature space. In the proposed D-DPCC network, we design a Multi-scale Motion Fusion (MMF) module to accurately estimate the 3D optical flow between the feature representations of adjacent point cloud frames. Specifically, we utilize a 3D

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Li, Li, Zhu Li, Vladyslav Zakharchenko, and Jianle Chen. "Advanced 3D Motion Prediction for Video Based Point Cloud Attributes Compression." In 2019 Data Compression Conference (DCC). IEEE, 2019. http://dx.doi.org/10.1109/dcc.2019.00058.

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Nguyen, Dat Thanh, Maurice Quach, Giuseppe Valenzise, and Pierre Duhamel. "Learning-Based Lossless Compression of 3D Point Cloud Geometry." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414763.

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Li, Zhe, Lanyi He, Wenjie Zhu, Yiling Xu, Jun Sun, and Le Yang. "3D Point Cloud Attribute Compression Based on Cylindrical Projection." In 2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE, 2019. http://dx.doi.org/10.1109/bmsb47279.2019.8971837.

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Reports on the topic "3D Point cloud Compression"

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Blundell, S., and Philip Devine. Creation, transformation, and orientation adjustment of a building façade model for feature segmentation : transforming 3D building point cloud models into 2D georeferenced feature overlays. Engineer Research and Development Center (U.S.), 2020. http://dx.doi.org/10.21079/11681/35115.

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Habib, Ayman, Darcy M. Bullock, Yi-Chun Lin, and Raja Manish. Road Ditch Line Mapping with Mobile LiDAR. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317354.

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Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires mapping of the ditch profile to identify areas requiring excavation of long-term sediment accumulation. High-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) provide an opportunity for effective monitoring of roadside ditches and performing hydrological analyses. This study evaluated the applicability of mobile LiDAR for mapping roadside ditches for slope and drainage analyses. The performance o

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