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Journal articles on the topic 'Photon tracing on GPU'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

Blyth, Simon. "Opticks : GPU Optical Photon Simulation for Particle Physics using NVIDIA® OptiXTM." EPJ Web of Conferences 214 (2019): 02027. http://dx.doi.org/10.1051/epjconf/201921402027.

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Opticks is an open source project that integrates the NVIDIA OptiX GPU ray tracing engine with Geant4 toolkit based simulations. Massive parallelism brings drastic performance improvements with optical photon simulation speedup expected to exceed 1000 times Geant4 with workstation GPUs. Optical physics processes of scattering, absorption, scintillator reemission and boundary processes are implemented as CUDA OptiX programs based on the Geant4 implementations. Wavelength-dependent material and surface properties as well as inverse cumulative distribution functions for reemission are interleaved into GPU textures providing fast interpolated property lookup or wavelength generation. OptiX handles the creation and application of a choice of acceleration structures such as boundary volume hierarchies and the transparent use of multiple GPUs. A major recent advance is the implementation of GPU ray tracing of complex constructive solid geometry shapes, enabling automated translation of Geant4 geometries to the GPU without approximation. Using common initial photons and random number sequences allows the Opticks and Geant4 simulations to be run point-by-point aligned. Aligned running has reached near perfect equivalence with test geometries.

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2

Blyth, Simon. "Integration of JUNO simulation framework with Opticks: GPU accelerated optical propagation via NVIDIA® OptiX™." EPJ Web of Conferences 251 (2021): 03009. http://dx.doi.org/10.1051/epjconf/202125103009.

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Opticks is an open source project that accelerates optical photon simulation by integrating NVIDIA GPU ray tracing, accessed via NVIDIA OptiX, with Geant4 toolkit based simulations. A single NVIDIA Turing architecture GPU has been measured to provide optical photon simulation speedup factors exceeding 1500 times single threaded Geant4 with a full JUNO analytic GPU geometry automatically translated from the Geant4 geometry. Optical physics processes of scattering, absorption, scintillator reemission and boundary processes are implemented within CUDA OptiX programs based on the Geant4 implementations. Wavelength-dependent material and surface properties as well as inverse cumulative distribution functions for reemission are interleaved into GPU textures providing fast interpolated property lookup or wavelength generation. In this work we describe major recent developments to facilitate integration of Opticks with the JUNO simulation framework including on GPU collection effciency hit culling which substantially reduces both the CPU memory needed for photon hits and copying overheads. Also progress with the migration of Opticks to the all new NVIDIA OptiX 7 API is described.

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3

Blyth, Simon. "Meeting the challenge of JUNO simulation with Opticks: GPU optical photon acceleration via NVIDIA® OptiXTM." EPJ Web of Conferences 245 (2020): 11003. http://dx.doi.org/10.1051/epjconf/202024511003.

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Opticks is an open source project that accelerates optical photon simulation by integrating NVIDIA GPU ray tracing, accessed via NVIDIA OptiX, with Geant4 toolkit based simulations. A single NVIDIA Turing architecture GPU has been measured to provide optical photon simulation speedup factors exceeding 1500 times single threaded Geant4 with a full JUNO analytic GPU geometry automatically translated from the Geant4 geometry. Optical physics processes of scattering, absorption, scintillator reemission and boundary processes are implemented within CUDA OptiX programs based on the Geant4 implementations. Wavelength-dependent material and surface properties as well as inverse cumulative distribution functions for reemission are interleaved into GPU textures providing fast interpolated property lookup or wavelength generation. Major recent developments enable Opticks to benefit from ray trace dedicated RT cores available in NVIDIA RTX series GPUs. Results of extensive validation tests are presented.

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4

Parker, Steven G., Heiko Friedrich, David Luebke, et al. "GPU ray tracing." Communications of the ACM 56, no. 5 (2013): 93–101. http://dx.doi.org/10.1145/2447976.2447997.

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5

Weiskopf, Daniel, Tobias Schafhitzel, and Thomas Ertl. "GPU-Based Nonlinear Ray Tracing." Computer Graphics Forum 23, no. 3 (2004): 625–33. http://dx.doi.org/10.1111/j.1467-8659.2004.00794.x.

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6

Hu, Wei, Yangyu Huang, Fan Zhang, Guodong Yuan, and Wei Li. "Ray tracing via GPU rasterization." Visual Computer 30, no. 6-8 (2014): 697–706. http://dx.doi.org/10.1007/s00371-014-0968-8.

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7

Pérard-Gayot, Arsène, Javor Kalojanov, and Philipp Slusallek. "GPU Ray Tracing using Irregular Grids." Computer Graphics Forum 36, no. 2 (2017): 477–86. http://dx.doi.org/10.1111/cgf.13142.

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8

Couturier, David, and Michel R. Dagenais. "LTTng CLUST: A System-Wide Unified CPU and GPU Tracing Tool for OpenCL Applications." Advances in Software Engineering 2015 (August 19, 2015): 1–14. http://dx.doi.org/10.1155/2015/940628.

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As computation schemes evolve and many new tools become available to programmers to enhance the performance of their applications, many programmers started to look towards highly parallel platforms such as Graphical Processing Unit (GPU). Offloading computations that can take advantage of the architecture of the GPU is a technique that has proven fruitful in recent years. This technology enhances the speed and responsiveness of applications. Also, as a side effect, it reduces the power requirements for those applications and therefore extends portable devices battery life and helps computing clusters to run more power efficiently. Many performance analysis tools such as LTTng, strace and SystemTap already allow Central Processing Unit (CPU) tracing and help programmers to use CPU resources more efficiently. On the GPU side, different tools such as Nvidia’s Nsight, AMD’s CodeXL, and third party TAU and VampirTrace allow tracing Application Programming Interface (API) calls and OpenCL kernel execution. These tools are useful but are completely separate, and none of them allow a unified CPU-GPU tracing experience. We propose an extension to the existing scalable and highly efficient LTTng tracing platform to allow unified tracing of GPU along with CPU’s full tracing capabilities.

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9

Liu, Baoquan, Li-Yi Wei, Xu Yang, et al. "Non-Linear Beam Tracing on a GPU." Computer Graphics Forum 30, no. 8 (2011): 2156–69. http://dx.doi.org/10.1111/j.1467-8659.2011.01905.x.

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10

Ohkawara, Masaru, Hideo Saito, and Issei Fujishiro. "Experiencing GPU path tracing in online courses." Graphics and Visual Computing 4 (June 2021): 200022. http://dx.doi.org/10.1016/j.gvc.2021.200022.

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11

Jaroš, Milan, Lubomír Říha, Petr Strakoš, and Matěj Špeťko. "GPU Accelerated Path Tracing of Massive Scenes." ACM Transactions on Graphics 40, no. 2 (2021): 1–17. http://dx.doi.org/10.1145/3447807.

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This article presents a solution to path tracing of massive scenes on multiple GPUs. Our approach analyzes the memory access pattern of a path tracer and defines how the scene data should be distributed across up to 16 GPUs with minimal effect on performance. The key concept is that the parts of the scene that have the highest amount of memory accesses are replicated on all GPUs. We propose two methods for maximizing the performance of path tracing when working with partially distributed scene data. Both methods work on the memory management level and therefore path tracer data structures do not have to be redesigned, making our approach applicable to other path tracers with only minor changes in their code. As a proof of concept, we have enhanced the open-source Blender Cycles path tracer. The approach was validated on scenes of sizes up to 169 GB. We show that only 1–5% of the scene data needs to be replicated to all machines for such large scenes. On smaller scenes we have verified that the performance is very close to rendering a fully replicated scene. In terms of scalability we have achieved a parallel efficiency of over 94% using up to 16 GPUs.

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12

He Jin, Fang Zhiyi, Ji Liang, Cai Ruicheng, and Chen Lin. "GPU-Based Research of Highly Efficient Ray Tracing." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, no. 10 (2011): 207–15. http://dx.doi.org/10.4156/aiss.vol3.issue10.26.

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13

Yang, Xin, Duan-qing Xu, Lei Zhao, and Bing Yang. "Complex shading efficiently for ray tracing on GPU." Multimedia Tools and Applications 74, no. 3 (2013): 1091–106. http://dx.doi.org/10.1007/s11042-013-1712-5.

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14

Kuchelmeister, Daniel, Thomas Müller, Marco Ament, Günter Wunner, and Daniel Weiskopf. "GPU-based four-dimensional general-relativistic ray tracing." Computer Physics Communications 183, no. 10 (2012): 2282–90. http://dx.doi.org/10.1016/j.cpc.2012.04.030.

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15

Hachisuka, Toshiya, and Henrik Wann Jensen. "Robust adaptive photon tracing using photon path visibility." ACM Transactions on Graphics 30, no. 5 (2011): 1–11. http://dx.doi.org/10.1145/2019627.2019633.

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16

Hofmann, Nikolai, Jon Hasselgren, Petrik Clarberg, and Jacob Munkberg. "Interactive Path Tracing and Reconstruction of Sparse Volumes." Proceedings of the ACM on Computer Graphics and Interactive Techniques 4, no. 1 (2021): 1–19. http://dx.doi.org/10.1145/3451256.

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We combine state-of-the-art techniques into a system for high-quality, interactive rendering of participating media. We leverage unbiased volume path tracing with multiple scattering, temporally stable neural denoising and NanoVDB [Museth 2021], a fast, sparse voxel tree data structure for the GPU, to explore what performance and image quality can be obtained for rendering volumetric data. Additionally, we integrate neural adaptive sampling to significantly improve image quality at a fixed sample budget. Our system runs at interactive rates at 1920 × 1080 on a single GPU and produces high quality results for complex dynamic volumes.

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17

Baack, D., and W. Rhode. "GPU based photon propagation for CORSIKA 8." Journal of Physics: Conference Series 1690 (December 2020): 012073. http://dx.doi.org/10.1088/1742-6596/1690/1/012073.

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18

Batagelo, Harlen Costa, and João Paulo Gois. "GPU-Based Sphere Tracing for Radial Basis Function Implicits." International Journal of Image and Graphics 14, no. 01n02 (2014): 1450004. http://dx.doi.org/10.1142/s0219467814500041.

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Ray tracing of implicit surfaces based on radial basis functions can demand high computational cost in the presence of a large number of radial centers. Recently, it was presented the least squares hermite radial basis functions (LS-HRBF) Implicits, a method for implicit surface reconstruction from Hermitian data (points equipped with their normal vectors) which makes use of iterative center selection in order to reduce the number of centers. In the present work, we propose an antialiazed sphere tracing algorithm fully implemented in OpenGL Shader Language for ray tracing LS-HRBF Implicits, which exploits a regular partition of unity for strong parallelization. We show that interactive frame rates can be achieved for surfaces composed of thousands of centers even when rendering effects such as cube mapping, soft shadows and ambient occlusion are used.

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19

Martins, Jorge R., Vasco S. Costa, and João M. Pereira. "Efficient Hair Rendering with a GPU Cone Tracing Approach." International Journal of Creative Interfaces and Computer Graphics 8, no. 1 (2017): 1–19. http://dx.doi.org/10.4018/ijcicg.2017010101.

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Rendering human hair can be a hard task because of the required high super-sampling rate to render thin hair fibers without noticeable aliasing. Additionally, the current state-of-the-art bounding volume hierarchies (BVHs) are not suitable to hair rendering. In fact, the axis-aligned bounding boxes (AABBs) do not tightly bind hair primitives which impacts negatively the intersection tests activity. Both limitations can degrade severely the rendering performance so described in this article, a cone tracing GPU approach coupled with a hybrid bounding volume hierarchy to tackle these problems. The hybrid BVH makes use of both oriented and axis aligned bounding boxes. It is shown that the experiment is able to drastically reduce the super-sampling required to produce aliasing free images while minimizing the number of intersection tests and achieving speedups of up to 4, depending on the scene.

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20

Heinrich, H., P. Ziegenhein, C. P. Kamerling, H. Froening, and U. Oelfke. "GPU-accelerated ray-tracing for real-time treatment planning." Journal of Physics: Conference Series 489 (March 24, 2014): 012050. http://dx.doi.org/10.1088/1742-6596/489/1/012050.

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21

Horiuchi, Shuma, Shuhei Yoshida, and Manabu Yamamoto. "Fast GPU-based ray tracing in radial GRIN lenses." Applied Optics 53, no. 19 (2014): 4343. http://dx.doi.org/10.1364/ao.53.004343.

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22

Szirmay-Kalos, László, Barnabás Aszódi, István Lazányi, and Mátyás Premecz. "Approximate Ray-Tracing on the GPU with Distance Impostors." Computer Graphics Forum 24, no. 3 (2005): 695–704. http://dx.doi.org/10.1111/j.1467-8659.2005.0m894.x.

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23

Zins, Pierre, and Michel Dagenais. "Tracing and Profiling Machine Learning Dataflow Applications on GPU." International Journal of Parallel Programming 47, no. 5-6 (2019): 973–1013. http://dx.doi.org/10.1007/s10766-019-00630-5.

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24

Cheng, Sining, Huiyan Qu, and Xianjun Chen. "Ray tracing collision detection based on GPU pipeline reorganization." Journal of Physics: Conference Series 1732 (January 2021): 012057. http://dx.doi.org/10.1088/1742-6596/1732/1/012057.

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25

Collin, Charly, Mickaël Ribardière, Adrien Gruson, Rémi Cozot, Sumanta Pattanaik, and Kadi Bouatouch. "Visibility-driven progressive volume photon tracing." Visual Computer 29, no. 9 (2013): 849–59. http://dx.doi.org/10.1007/s00371-013-0845-x.

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26

Zheng, Quan, and Chang-Wen Zheng. "Visual importance-based adaptive photon tracing." Visual Computer 31, no. 6-8 (2015): 1001–10. http://dx.doi.org/10.1007/s00371-015-1104-0.

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27

van Aart, Evert, Neda Sepasian, Andrei Jalba, and Anna Vilanova. "CUDA-Accelerated Geodesic Ray-Tracing for Fiber Tracking." International Journal of Biomedical Imaging 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/698908.

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Diffusion Tensor Imaging (DTI) allows to noninvasively measure the diffusion of water in fibrous tissue. By reconstructing the fibers from DTI data using a fiber-tracking algorithm, we can deduce the structure of the tissue. In this paper, we outline an approach to accelerating such a fiber-tracking algorithm using a Graphics Processing Unit (GPU). This algorithm, which is based on the calculation of geodesics, has shown promising results for both synthetic and real data, but is limited in its applicability by its high computational requirements. We present a solution which uses the parallelism offered by modern GPUs, in combination with the CUDA platform by NVIDIA, to significantly reduce the execution time of the fiber-tracking algorithm. Compared to a multithreaded CPU implementation of the same algorithm, our GPU mapping achieves a speedup factor of up to 40 times.

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28

Fazliddinovich, Mekhriddin Rakhimov, and Yalew Kidane Tolcha. "Parallel Processing of Ray Tracing on GPU with Dynamic Pipelining." International Journal of Signal Processing Systems 4, no. 3 (2016): 209–13. http://dx.doi.org/10.18178/ijsps.4.3.209-213.

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29

Pajot, Anthony, Loïc Barthe, Mathias Paulin, and Pierre Poulin. "Combinatorial Bidirectional Path-Tracing for Efficient Hybrid CPU/GPU Rendering." Computer Graphics Forum 30, no. 2 (2011): 315–24. http://dx.doi.org/10.1111/j.1467-8659.2011.01863.x.

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30

de Greef, M., J. Crezee, J. C. van Eijk, R. Pool, and A. Bel. "Accelerated ray tracing for radiotherapy dose calculations on a GPU." Medical Physics 36, no. 9Part1 (2009): 4095–102. http://dx.doi.org/10.1118/1.3190156.

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31

Singh, J. M., and P. J. Narayanan. "Real-Time Ray Tracing of Implicit Surfaces on the GPU." IEEE Transactions on Visualization and Computer Graphics 16, no. 2 (2010): 261–72. http://dx.doi.org/10.1109/tvcg.2009.41.

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32

Popov, Stefan, Johannes Günther, Hans-Peter Seidel, and Philipp Slusallek. "Stackless KD-Tree Traversal for High Performance GPU Ray Tracing." Computer Graphics Forum 26, no. 3 (2007): 415–24. http://dx.doi.org/10.1111/j.1467-8659.2007.01064.x.

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33

Costa, Vasco, João Madeiras Pereira, and Joaquim A. Jorge. "Accelerating Occlusion Rendering on a GPU via Ray Classification." International Journal of Creative Interfaces and Computer Graphics 6, no. 2 (2015): 1–17. http://dx.doi.org/10.4018/ijcicg.2015070101.

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Accurately rendering occlusions is required when ray-tracing objects to achieve more realistic rendering of scenes. Indeed, soft phenomena such as shadows and ambient occlusion can be achieved with stochastic ray tracing techniques. However, computing randomized incoherent ray-object intersections can be inefficient. This is problematic in Graphics Processing Unit (GPU) applications, where thread divergence can significantly lower throughput. The authors show how this issue can be mitigated using classification techniques that sort rays according to their spatial characteristics. Still, classifying occlusion terms requires sorting millions of rays. This is offset by savings in rendering time, which result from a more coherent ray distribution. The authors survey and test different ray classification techniques to identify the most effective. The best results were achieved when sorting rays using a compress-sort-decompress approach using 32-bit hash keys.

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34

Perrotte, Lancelot, and Guillaume Saupin. "Fast GPU perspective grid construction and triangle tracing for exhaustive ray tracing of highly coherent rays." International Journal of High Performance Computing Applications 26, no. 3 (2011): 192–202. http://dx.doi.org/10.1177/1094342011403785.

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35

MENDONÇA, J. T., K. HIZANIDIS, D. J. FRANTZESKAKIS, L. OLIVEIRA e SILVA, and J. L. VOMVORIDIS. "Covariant formulation of photon acceleration." Journal of Plasma Physics 58, no. 4 (1997): 647–54. http://dx.doi.org/10.1017/s0022377897006168.

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We present a covariant theory of photon acceleration in time-varying plasmas. This is a ray-tracing model for frequency upshift of waves interacting with relativistic plasma perturbations, such as ionization fronts and plasma wakefields. This theory explores the formal analogy between a photon in a plasma and a relativistic particle with a finite rest mass. The covariant ray-tracing theory is applied to the case of photon acceleration by a modulated wakefield. Threshold criteria for transition to chaos are derived.

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36

Yu, Xuan, Rui Wang, and Jingyi Yu. "Interactive Glossy Reflections using GPU‐based Ray Tracing with Adaptive LOD." Computer Graphics Forum 27, no. 7 (2008): 1987–96. http://dx.doi.org/10.1111/j.1467-8659.2008.01348.x.

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37

Áfra, Attila T., and László Szirmay-Kalos. "Stackless Multi-BVH Traversal for CPU, MIC and GPU Ray Tracing." Computer Graphics Forum 33, no. 1 (2013): 129–40. http://dx.doi.org/10.1111/cgf.12259.

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38

Inui, Masatomo, Kohei Kaba, and Nobuyuki Umezu. "Fast Dexelization of Polyhedral Models Using Ray-Tracing Cores of GPU." Computer-Aided Design and Applications 18, no. 4 (2020): 786–98. http://dx.doi.org/10.14733/cadaps.2021.786-798.

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39

Stone, John E., Melih Sener, Kirby L. Vandivort, et al. "Atomic detail visualization of photosynthetic membranes with GPU-accelerated ray tracing." Parallel Computing 55 (July 2016): 17–27. http://dx.doi.org/10.1016/j.parco.2015.10.015.

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40

Celestino, Simone, Giuliano Laccetti, Marco Lapegna, and Diego Romano. "A bidirectional path tracing method for global illumination rendering on GPU." Applied Mathematical Sciences 8 (2014): 6783–90. http://dx.doi.org/10.12988/ams.2014.49694.

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41

Creaner, Oisín, Simon Blyth, Sam Eriksen, Lisa Gerhardt, Maria Elena Monzani, and Quentin Riffard. "GPU simulation with Opticks: The future of optical simulations for LZ." EPJ Web of Conferences 251 (2021): 03037. http://dx.doi.org/10.1051/epjconf/202125103037.

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The LZ collaboration aims to directly detect dark matter by using a liquid xenon Time Projection Chamber (TPC). In order to probe the dark matter signal, observed signals are compared with simulations that model the detector response. The most computationally expensive aspect of these simulations is the propagation of photons in the detector’s sensitive volume. For this reason, we propose to offload photon propagation modelling to the Graphics Processing Unit (GPU), by integrating Opticks into the LZ simulations workflow. Opticks is a system which maps Geant4 geometry and photon generation steps to NVIDIA’s OptiX GPU raytracing framework. This paradigm shift could simultaneously achieve a massive speed-up and an increase in accuracy for LZ simulations. By using the technique of containerization through Shifter, we will produce a portable system to harness the NERSC supercomputing facilities, including the forthcoming Perlmutter supercomputer, and enable the GPU processing to handle different detector configurations. Prior experience with using Opticks to simulate JUNO indicates the potential for speed-up factors over 1000× for LZ, and by extension other experiments requiring photon propagation simulations.

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42

Gruson, Adrien, Mickaël Ribardière, Martin Šik, et al. "A Spatial Target Function for Metropolis Photon Tracing." ACM Transactions on Graphics 36, no. 1 (2017): 1–13. http://dx.doi.org/10.1145/2963097.

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43

Gruson, Adrien, Mickaël Ribardière, Martin Šik, et al. "A Spatial Target Function for Metropolis Photon Tracing." ACM Transactions on Graphics 36, no. 4 (2017): 1. http://dx.doi.org/10.1145/3072959.2963097.

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44

Gruson, Adrien, Mickaël Ribardière, Martin Šik, et al. "A spatial target function for metropolis photon tracing." ACM Transactions on Graphics 36, no. 4 (2017): 1. http://dx.doi.org/10.1145/3072959.3126811.

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45

Wilkie, Alexander, Robert F. Tobler, and Werner Purgathofer. "Orientation lightmaps for photon tracing in complex environments." Visual Computer 17, no. 5 (2001): 318–27. http://dx.doi.org/10.1007/s003710100101.

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46

JIANG, CHAO, HENG HE, PENGCHENG LI, and QINGMING LUO. "GRAPHICS PROCESSING UNIT CLUSTER ACCELERATED MONTE CARLO SIMULATION OF PHOTON TRANSPORT IN MULTI-LAYERED TISSUES." Journal of Innovative Optical Health Sciences 05, no. 02 (2012): 1250004. http://dx.doi.org/10.1142/s1793545812500046.

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We present a graphics processing unit (GPU) cluster-based Monte Carlo simulation of photon transport in multi-layered tissues. The cluster is composed of multiple computing nodes in a local area network where each node is a personal computer equipped with one or several GPU(s) for parallel computing. In this study, the MPI (Message Passing Interface), the OpenMP (Open Multi-Processing) and the CUDA (Compute Unified Device Architecture) technologies are employed to develop the program. It is demonstrated that this designing runs roughly N times faster than that using single GPU when the GPUs within the cluster are of the same type, where N is the total number of the GPUs within the cluster.

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47

Mirzapour, M., K. Hadad, R. Faghihi, R. J. Hamilton, and C. J. Watchman. "Fast Monte-Carlo Photon Transport Employing GPU-Based Parallel Computation." IEEE Transactions on Radiation and Plasma Medical Sciences 4, no. 4 (2020): 450–60. http://dx.doi.org/10.1109/trpms.2020.2972202.

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48

Garanzha, Kirill, and Charles Loop. "Fast Ray Sorting and Breadth-First Packet Traversal for GPU Ray Tracing." Computer Graphics Forum 29, no. 2 (2010): 289–98. http://dx.doi.org/10.1111/j.1467-8659.2009.01598.x.

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49

Zhou, Bo, Kai Xiao, Danny Z. Chen, and X. Sharon Hu. "GPU-optimized volume ray tracing for massive numbers of rays in radiotherapy." ACM Transactions on Embedded Computing Systems 13, no. 3 (2013): 1–17. http://dx.doi.org/10.1145/2539036.2539038.

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50

Bourgoin, A., M. Zannoni, and P. Tortora. "Analytical ray-tracing in planetary atmospheres." Astronomy & Astrophysics 624 (April 2019): A41. http://dx.doi.org/10.1051/0004-6361/201834962.

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Context. Ground-based astro-geodetic observations and atmospheric radio occultations are two examples of observational techniques requiring a scrutiny analysis of atmospheric refraction. In both cases, the measured changes of the observables are geometrically related to changes in the photon path through the refractive profile of the crossed medium. Therefore, having a clear knowledge of how the refractivity governs the photon path evolution is of prime importance to clearly understand observational features. Aims. We analytically performed the integration of the photon path and the light time of rays traveling across a non-spherically symmetric planetary atmosphere. Methods. Assuming that the atmospheric refraction evolves linearly with the Newtonian potential, we derived an exact solution to the equations of geometrical optics. By varying the solution’s arbitrary constants of integration, we reformulated the equation of geometrical optics into a new set of osculating equations describing the constants’ evolution following any changes in the refractive profile. We have highlighted the capabilities of the formalism, carrying out five realistic applications in which we derived analytical expressions. Finally, we assessed the accuracy by comparing the solution to results from a numerical integration of the equations of geometrical optics in the presence of a quadrupolar moment (J2). Results. Analytical expressions for the light time and the refractive bending are given with relative errors at the level of one part in 108 and one part in 105, for typical values of the refractivity and J2 at levels of 10−4 and 10−2, respectively. Conclusions. The establishment of the osculating equations for the ray propagation has two main advantages. Firstly, it provides an easy and comprehensive geometrical picture for interpreting the photon path. Secondly, it allows the analytical solving of the ray propagation in the presence of non-radial dependencies in the refractive profile.

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