# Joint Depth and Reflectivity Estimation using Single-Photon LiDAR

arXiv: https://arxiv.org/abs/2505.13250 · HTML: https://mlsanity.com/paper/2505.13250/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 71 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [MaxViT: Multi-Axis Vision Transformer](https://mlsanity.com/paper/2204.01697/) (2022)
- [Recurrent Video Restoration Transformer with Guided Deformable Attention](https://mlsanity.com/paper/2206.02146/) (2022)
- [Deep Depth Completion of a Single RGB-D Image](https://mlsanity.com/paper/1803.09326/) (2018)
- [Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems](https://mlsanity.com/paper/2004.08467/) (2020)
- [CompletionFormer: Depth Completion with Convolutions and Vision Transformers](https://mlsanity.com/paper/2304.13030/) (2023)
- [A megapixel time-gated SPAD image sensor for 2D and 3D imaging applications](https://mlsanity.com/paper/1912.12910/) (2019)
- [Quanta Burst Photography](https://mlsanity.com/paper/2006.11840/) (2020)
- [Revisiting Temporal Alignment for Video Restoration](https://mlsanity.com/paper/2111.15288/) (2021)
- [Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones](https://mlsanity.com/paper/2204.05532/) (2022)
- [Photon-Flooded Single-Photon 3D Cameras](https://mlsanity.com/paper/1903.08347/) (2019)
- [Dynamic Low-light Imaging with Quanta Image Sensors](https://mlsanity.com/paper/2007.08614/) (2020)
- [Multi-Scale Memory-Based Video Deblurring](https://mlsanity.com/paper/2204.02977/) (2022)
- [Dead Time Compensation for High-Flux Ranging](https://mlsanity.com/paper/1810.11145/) (2018)
- [Robust super-resolution depth imaging via a multi-feature fusion deep network](https://mlsanity.com/paper/2011.11444/) (2020)
- [High-speed object detection with a single-photon time-of-flight image sensor](https://mlsanity.com/paper/2107.13407/) (2021)
- [Bayesian 3D Reconstruction of Complex Scenes from Single-Photon Lidar Data](https://mlsanity.com/paper/1810.11633/) (2018)
- [Object Depth Profile and Reflectivity Restoration from Sparse Single-Photon Data Acquired in Underwater Environments](https://mlsanity.com/paper/1608.06143/) (2016)
- [Resolution Limit of Single-Photon LiDAR](https://mlsanity.com/paper/2403.17719/) (2024)
- [Streaming quanta sensors for online, high-performance imaging and vision](https://mlsanity.com/paper/2406.00859/) (2024)
- [Quanta Video Restoration](https://mlsanity.com/paper/2410.14994/) (2024)
- [A Bayesian approach to denoising of single-photon binary images](https://mlsanity.com/paper/1609.04862/) (2016)
- [bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction](https://mlsanity.com/paper/2410.23247/) (2024)
- [Fast online 3D reconstruction of dynamic scenes from individual single-photon detection events](https://mlsanity.com/paper/1905.02944/) (2019)
- [Parametric Modeling and Estimation of Photon Registrations for 3D Imaging](https://mlsanity.com/paper/2407.02712/) (2024)
- [Analysis and Improvement of Rank-Ordered Mean Algorithm in Single-Photon LiDAR](https://mlsanity.com/paper/2407.20399/) (2024)

## Abstract (arXiv)

Single-Photon Light Detection and Ranging (SP-LiDAR is emerging as a leading technology for long-range, high-precision 3D vision tasks. In SP-LiDAR, timestamps encode two complementary pieces of information: pulse travel time (depth) and the number of photons reflected by the object (reflectivity). Existing SP-LiDAR reconstruction methods typically recover depth and reflectivity separately or sequentially use one modality to estimate the other. Moreover, the conventional 3D histogram construction is effective mainly for slow-moving or stationary scenes. In dynamic scenes, however, it is more efficient and effective to directly process the timestamps. In this paper, we introduce an estimation method to simultaneously recover both depth and reflectivity in fast-moving scenes. We offer two contributions: (1) A theoretical analysis demonstrating the mutual correlation between depth and reflectivity and the conditions under which joint estimation becomes beneficial. (2) A novel reconstruction method, "SPLiDER", which exploits the shared information to enhance signal recovery. On both synthetic and real SP-LiDAR data, our method outperforms existing approaches, achieving superior joint reconstruction quality.