# All-photon Polarimetric Time-of-Flight Imaging

arXiv: https://arxiv.org/abs/2112.09278 · HTML: https://mlsanity.com/paper/2112.09278/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 17 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [Real-Time Global Illumination Decomposition of Videos](https://mlsanity.com/paper/1908.01961/) (2019)
- [Snapshot Difference Imaging using Time-of-Flight Sensors](https://mlsanity.com/paper/1705.07108/) (2017)

## Built on by (most cited first, 12 of 12)

- [NeISF: Neural Incident Stokes Field for Geometry and Material Estimation](https://mlsanity.com/paper/2311.13187/) (2023)
- [Polarization Wavefront Lidar: Learning Large Scene Reconstruction from Polarized Wavefronts](https://mlsanity.com/paper/2406.03461/) (2024)
- [Deep Polarization Cues for Single-shot Shape and Subsurface Scattering Estimation](https://mlsanity.com/paper/2407.08149/) (2024)
- [Differentiable Display Photometric Stereo](https://mlsanity.com/paper/2306.13325/) (2023)
- [Event Ellipsometer: Event-based Mueller-Matrix Video Imaging](https://mlsanity.com/paper/2411.17313/) (2024)
- [Polarimetric BSSRDF Acquisition of Dynamic Faces](https://mlsanity.com/paper/2501.01980/) (2024)
- [Benchmarking Burst Super-Resolution for Polarization Images: Noise Dataset and Analysis](https://mlsanity.com/paper/2503.18705/) (2025)
- [Polarimetric Light Transport Analysis for Specular Inter-reflection](https://mlsanity.com/paper/2312.04140/) (2023)
- [Spatial Polarization Multiplexing: Single-Shot Invisible Shape and Reflectance Recovery](https://mlsanity.com/paper/2504.13177/) (2025)
- [Revisiting Shape from Polarization in the Era of Vision Foundation Models](https://mlsanity.com/paper/2603.04817/) (2026)
- [PhyGaP: Physically-Grounded Gaussians with Polarization Cues](https://mlsanity.com/paper/2603.14001/) (2026)
- [End-to-End Optimization of Polarimetric Measurement and Material Classifier](https://mlsanity.com/paper/2603.20519/) (2026)

## Abstract (arXiv)

Time-of-flight (ToF) sensors provide an imaging modality fueling diverse applications, including LiDAR in autonomous driving, robotics, and augmented reality. Conventional ToF imaging methods estimate the depth by sending pulses of light into a scene and measuring the ToF of the first-arriving photons directly reflected from a scene surface without any temporal delay. As such, all photons following this first response are typically considered as unwanted noise. In this paper, we depart from the principle of using first-arriving photons and propose an all-photon ToF imaging method by incorporating the temporal-polarimetric analysis of first- and late-arriving photons, which possess rich scene information about its geometry and material. To this end, we propose a novel temporal-polarimetric reflectance model, an efficient capture method, and a reconstruction method that exploits the temporal-polarimetric changes of light reflected by the surface and sub-surface reflection. The proposed all-photon polarimetric ToF imaging method allows for acquiring depth, surface normals, and material parameters of a scene by utilizing all photons captured by the system, whereas conventional ToF imaging only obtains coarse depth from the first-arriving photons. We validate our method in simulation and experimentally with a prototype.