Transientangelo: Few-Viewpoint Surface Reconstruction using Single-Photon Lidar

Weihan Luo, Anagh Malik, David B Lindell; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 8712-8722

We consider the problem of few-viewpoint 3D surface reconstruction using raw measurements from a lidar system. Lidar captures 3D scene geometry by emitting pulses of light to a target and recording the speed-of-light time delay of the reflected light. However conventional lidar systems do not output the raw captured waveforms of backscattered light; instead they preprocess these data into a 3D point cloud. Since this procedure typically does not accurately model the noise statistics of the system exploit spatial priors or incorporate information about downstream tasks it ultimately discards useful information that is encoded in raw measurements of backscattered light. Here we propose to leverage raw measurements captured with a single-photon lidar system from multiple viewpoints to optimize a neural surface representation of a scene. The measurements consist of time-resolved photon count histograms or transients which capture information about backscattered light at picosecond time scales. Additionally we develop new regularization strategies that improve robustness to photon noise enabling accurate surface reconstruction with as few as 10 photons per pixel. Our method outperforms other techniques for few-viewpoint 3D reconstruction based on depth maps point clouds or conventional lidar as demonstrated in simulation and with captured data.