Physically Based Neural LiDAR Resimulation
Marcus R, Stamminger M (2026)
Publication Type: Conference contribution
Publication year: 2026
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 3727-3734
Conference Proceedings Title: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Event location: Gold Coast
ISBN: 9798331524180
DOI: 10.1109/ITSC60802.2025.11423645
Abstract
Methods for Novel View Synthesis (NVS) have recently found traction in the field of LiDAR simulation and large-scale 3D scene reconstruction. While solutions for faster rendering or handling dynamic scenes have been proposed, LiDAR specific effects remain insufficiently addressed. By explicitly modeling sensor characteristics such as rolling shutter, laser power variations, and intensity falloff, our method achieves more accurate LiDAR simulation compared to existing techniques. We demonstrate the effectiveness of our approach through quantitative and qualitative comparisons with state-of-the-art methods, as well as ablation studies that highlight the importance of each sensor model component. Beyond that, we show that our approach exhibits advanced resimulation capabilities, such as generating high resolution LiDAR scans in the camera perspective. Our code and the resulting dataset are available at https://github.com/richardmarcus/PBNLiDAR.
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How to cite
APA:
Marcus, R., & Stamminger, M. (2026). Physically Based Neural LiDAR Resimulation. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC (pp. 3727-3734). Gold Coast, AU: Institute of Electrical and Electronics Engineers Inc..
MLA:
Marcus, Richard, and Marc Stamminger. "Physically Based Neural LiDAR Resimulation." Proceedings of the 28th International Conference on Intelligent Transportation Systems, ITSC 2025, Gold Coast Institute of Electrical and Electronics Engineers Inc., 2026. 3727-3734.
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