TY - GEN
T1 - Shoot-Bounce-3D
T2 - 2025 SIGGRAPH Asia 2025 Conference Papers, SA 2025
AU - Klinghoffer, Tzofi
AU - Somasundaram, Siddharth
AU - Xiang, Xiaoyu
AU - Fan, Yuchen
AU - Richardt, Christian
AU - Dave, Akshat
AU - Raskar, Ramesh
AU - Ranjan, Rakesh
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/12/14
Y1 - 2025/12/14
N2 - 3D scene reconstruction from a single measurement is challenging, especially in the presence of occluded regions and specular materials, such as mirrors. We address these challenges by leveraging single-photon lidars. These lidars estimate depth from light that is emitted into the scene and reflected directly back to the sensor. However, they can also measure light that bounces multiple times in the scene before reaching the sensor. This multi-bounce light contains additional information that can be used to recover dense depth, occluded geometry, and material properties. Prior work with single-photon lidar, however, has only demonstrated these use cases when a laser sequentially illuminates one scene point at a time. We instead focus on the more practical - and challenging - scenario of illuminating multiple scene points simultaneously. The complexity of light transport due to the combined effects of multiplexed illumination, two-bounce light, shadows, and specular reflections is challenging to invert analytically. Instead, we propose a data-driven method to invert light transport in single-photon lidar. To enable this approach, we create the first large-scale simulated dataset of ∼100k lidar transients for indoor scenes. We use this dataset to learn a prior on complex light transport, enabling measured two-bounce light to be decomposed into the constituent contributions from each laser spot. Finally, we experimentally demonstrate how this decomposed light can be used to infer 3D geometry in scenes with occlusions and mirrors from a single measurement. Our code and dataset are released on our project webpage.
AB - 3D scene reconstruction from a single measurement is challenging, especially in the presence of occluded regions and specular materials, such as mirrors. We address these challenges by leveraging single-photon lidars. These lidars estimate depth from light that is emitted into the scene and reflected directly back to the sensor. However, they can also measure light that bounces multiple times in the scene before reaching the sensor. This multi-bounce light contains additional information that can be used to recover dense depth, occluded geometry, and material properties. Prior work with single-photon lidar, however, has only demonstrated these use cases when a laser sequentially illuminates one scene point at a time. We instead focus on the more practical - and challenging - scenario of illuminating multiple scene points simultaneously. The complexity of light transport due to the combined effects of multiplexed illumination, two-bounce light, shadows, and specular reflections is challenging to invert analytically. Instead, we propose a data-driven method to invert light transport in single-photon lidar. To enable this approach, we create the first large-scale simulated dataset of ∼100k lidar transients for indoor scenes. We use this dataset to learn a prior on complex light transport, enabling measured two-bounce light to be decomposed into the constituent contributions from each laser spot. Finally, we experimentally demonstrate how this decomposed light can be used to infer 3D geometry in scenes with occlusions and mirrors from a single measurement. Our code and dataset are released on our project webpage.
UR - https://www.scopus.com/pages/publications/105032506432
U2 - 10.1145/3757377.3763945
DO - 10.1145/3757377.3763945
M3 - Conference contribution
AN - SCOPUS:105032506432
T3 - Proceedings - SIGGRAPH Asia 2025 Conference Papers, SA 2025
BT - Proceedings - SIGGRAPH Asia 2025 Conference Papers, SA 2025
A2 - Spencer, Stephen N.
A2 - Komura, Taku
A2 - Wimmer, Michael
A2 - Fu, Hongbo
PB - Association for Computing Machinery, Inc
Y2 - 15 December 2025 through 18 December 2025
ER -