mlsanityData to 14 Oct 2026

Joint Depth and Reflectivity Estimation using Single-Photon LiDAR

arXiv 2505.13250 ↗First posted 19 May 2025cs.CVcited by 0

Not on the map. Why?

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.

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

29 of 71 references.

References · 29

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  4. 2024Depth Anything V2cited by 1,422
  5. 2022MaxViT: Multi-Axis Vision Transformercited by 476
  6. 2022Recurrent Video Restoration Transformer with Guided Deformable Attentioncited by 170
  7. 2018Deep Depth Completion of a Single RGB-D Imagecited by 110
  8. 2020Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systemscited by 100
  9. 2023CompletionFormer: Depth Completion with Convolutions and Vision Transformerscited by 93
  10. 2019A megapixel time-gated SPAD image sensor for 2D and 3D imaging applicationscited by 28
  11. 2020Quanta Burst Photographycited by 25
  12. 2021Revisiting Temporal Alignment for Video Restorationcited by 24
17 more
  1. 2022Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Onescited by 24
  2. 2019Photon-Flooded Single-Photon 3D Camerascited by 23
  3. 2020Dynamic Low-light Imaging with Quanta Image Sensorscited by 16
  4. 2022Multi-Scale Memory-Based Video Deblurringcited by 16
  5. 2018Dead Time Compensation for High-Flux Rangingcited by 9
  6. 2020Robust super-resolution depth imaging via a multi-feature fusion deep networkcited by 7
  7. 2021High-speed object detection with a single-photon time-of-flight image sensorcited by 7
  8. 2018Bayesian 3D Reconstruction of Complex Scenes from Single-Photon Lidar Datacited by 5
  9. 2016Object Depth Profile and Reflectivity Restoration from Sparse Single-Photon Data Acquired in Underwater Environmentscited by 4
  10. 2024Resolution Limit of Single-Photon LiDARcited by 3
  11. 2024Streaming quanta sensors for online, high-performance imaging and visioncited by 3
  12. 2024Quanta Video Restorationcited by 3
  13. 2016A Bayesian approach to denoising of single-photon binary imagescited by 2
  14. 2024bit2bit: 1-bit quanta video reconstruction via self-supervised photon predictioncited by 2
  15. 2019Fast online 3D reconstruction of dynamic scenes from individual single-photon detection eventscited by 1
  16. 2024Parametric Modeling and Estimation of Photon Registrations for 3D Imagingcited by 1
  17. 2024Analysis and Improvement of Rank-Ordered Mean Algorithm in Single-Photon LiDARcited by 1

Built on by

Not cited yet.