Autors: Georgiev, G. G., Lazarova, M. K.
Title: Cross-Platform Functional Programming Real-Time Path Tracing Rendering Engine
Keywords: Functional programming, Ray tracing, Real-time rendering, Rust programming, Vulkan

Abstract: With advancements in graphics processing unit (GPU) hardware capabilities so too has the complexity of programming interfaces for computer graphics risen, potentially leading to difficulties in regards to designing, implementing and maintaining high-performance GPU-accelerated applications. This paper presents an implementation of a real-time path tracing rendering engine written in Rust using a functional programming based approach, which aims to simplify the development, maintenance and cross-platform compatibility of the modern computer graphics pipeline.

References

  1. A. Barczak, and H. Wozniak, "Comparative Study on Game Engines", Studia Informatica, Systems and Information Technology, vol. 23, no. 1-2, pp. 5-24, 2020.
  2. E. Whiting, and Sh. Andrews, "Drift and Erosion in Software Architecture: Summary and Prevention Strategies", Proc. of 4th Int. Conf. on Inf. System and Data Mining, pp. 132-138, 2020.
  3. M. Springer, "Memory-Efficient Object-Oriented Programming on GPUs", arXiv preprint arXiv: 1908. 05845, 2019.
  4. W. Bugden, and A. Alahmar, "Rust: The Programming Language for Safety and Performance", arXiv preprint, arXiv: 2206. 05503, 2022.
  5. R. Pereira, et al., "Ranking Programming Languages by Energy Efficiency", Science of Computer Programming, vol. 205, 2021.
  6. W. Veytsman, et al., "Rewrite It in Rust: A Computational Physics Case Study", arXiv preprint, arXiv: 2410. 19146, 2024.
  7. E. Denisova, et al., "AR2T: Advanced realistic Rendering Technique for Biomedical Volumes", in H. Greenspan, H., et al. (edts.), "Medical Image Computing and Computer Assisted Intervention", Lecture Notes in Computer Science, Springer, Cham, vol. 14225, pp. 347-357, 2023.
  8. D. Lin, et al., "Generalized Resampled Importance Sampling: Foundations of ReSTIR", ACM Transactions on Graphics, vol. 41, no. 4, 2022.
  9. V. Sanzharov, et al., "Examination of the Nvidia RTX", CEUR Workshop Proceedings, vol. 2485, pp. 7-12, 2019.
  10. J. Hasselgren, N. Hofmann, and J. Munkberg, "Shape, Light, and Material Decomposition From Images Using Monte Carlo Rendering and Denoising", Advances in Neural Information Processing Systems, vol. 35, pp. 22856-22869, 2022.
  11. M. R. Ardakani, Matin, et al., "Framework for Denoising Monte Carlo Photon Transport Simulations Using Deep Learning", Journal of Biomedical Optics, vol. 27, no. 8, 2022.
  12. Y. Ouyang, et al., "ReSTIR GI: Path Resampling for Real-Time Path Tracing", Computer Graphics Forum, vol. 40, no. 8, 2021.

Issue

60th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2025 - Proceedings, 2025, Albania, https://doi.org/10.1109/ICEST66328.2025.11098413

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus