High-Flux Count-Free Single-Photon 3D Cameras
Kaustubh Sadekar, Vivek K Goyal, David Maier, Atul Ingle
Abstract
Single-photon cameras based on single-photon avalanche diode (SPAD) technology are gaining popularity for 3D sensing, thanks to their extreme sensitivity and time resolution. There are two key challenges with single-photon cameras that limit their widespread use: (i) they suffer from non-linear distortions called ''pile-up'' when operated in high-photon-flux conditions, and (ii) they generate a large volume of raw photon data, creating a severe data bottleneck at each sensor pixel. In this work, we show that while compressive capture techniques successfully mitigate data transfer challenges, they exacerbate the effects of dead-time distortion because they fail to retain sufficient information about the photon detection history to allow post-processing pile-up correction via existing methods. We propose a new computational-imaging method that combines free-running capture with an analysis-by-synthesis software pipeline to mitigate pile-up distortions. Our results with hardware emulations and full-scene and single-pixel simulations show that our method can reliably capture scene distance and reflectance over a wide range of illumination conditions. Our work will enable high-resolution SPAD cameras that are severely bandwidth-constrained to operate in real-world high-flux scenarios.
Create a lesson
Related papers
PhysVGGT: Feed-Forward Dense Physical Property Estimation from A Single Image
Sneha Paul, Guile Wu, Bingbing Liu et al.
NormLift: From Lifted Features To Semantic Reliability In 3D Gaussian Splatting
Yihan Zang, Da Li, Dominik Engel et al.
Decodable but Misrouted: Sparse Features Uncover a Readout Gap in Vision-Language Models for Harmful Meme Detection
Girish A. Koushik, Diptesh Kanojia, Helen Treharne
Copy What Is Seen, Generate What Is Not: Training-Free Anomaly-Aware Video Restoration
Zhida Qu, Shengchao Chen
Using OCR Heads to Verbalize Image Semantics
Sheridan Feucht, Benno Krojer, Sarah Wang et al.
DISTA-Net++: Rethinking Infrared Small Target Unmixing Beyond Sub-Pixel Separation
Mengze Xu, Zhu Liu, Weidong Sheng et al.