High-Flux Count-Free Single-Photon 3D Cameras

• URL: http://arxiv.org/abs/2608.18306v1
• Date: Tue, 18 Aug 2026 20:35:41 GMT
• Title: High-Flux Count-Free Single-Photon 3D Cameras
• Abstract summary: Single-photon cameras suffer from non-linear distortions called ''pile-up'' when operated in high-photon-flux conditions.<n>We propose a new computational-imaging method that combines free-running capture with an analysis-by-synthesis software pipeline.<n>Our work will enable high-resolution SPAD cameras that are severely bandwidth-constrained to operate in real-world high-flux scenarios.
• Score: 9.686585732583103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Plug-and-Play Algorithm for 3D Video Super-Resolution of Single-Photon LiDAR data [5.378429123269604]
  Single-photon avalanche diodes (SPADs) are advanced sensors capable of detecting individual photons and recording their arrival times with picosecond resolution.<n>We propose a novel computational imaging algorithm to improve the 3D reconstruction of moving scenes from SPAD data.
  arXiv  Detail & Related papers  (2024-12-12T16:33:06Z)
• FoveaSPAD: Exploiting Depth Priors for Adaptive and Efficient Single-Photon 3D Imaging [7.350208716861244]
  Single-photon avalanche diodes (SPADs) are an emerging image-sensing technology that offer many advantages such as extreme sensitivity and time resolution.<n>In this paper, we propose new algorithms and sensing policies that improve signal-to-noise ratio (SNR) and increase computing and memory efficiency.
  arXiv  Detail & Related papers  (2024-12-03T00:20:01Z)
• bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction [57.199618102578576]
  We propose bit2bit, a new method for reconstructing high-quality image stacks at original resolution from sparse binary quantatemporal image data. Inspired by recent work on Poisson denoising, we developed an algorithm that creates a dense image sequence from sparse binary photon data. We present a novel dataset containing a wide range of real SPAD high-speed videos under various challenging imaging conditions.
  arXiv  Detail & Related papers  (2024-10-30T17:30:35Z)
• Photon Inhibition for Energy-Efficient Single-Photon Imaging [19.816230454712585]
  Single-photon cameras (SPCs) are emerging as sensors of choice for challenging imaging applications. Yet, single-photon sensitivity in SPADs comes at a cost -- each photon detection consumes more energy than that of a CMOS camera. We propose a computational-imaging approach called emphphoton inhibition to address this challenge.
  arXiv  Detail & Related papers  (2024-09-26T23:19:44Z)
• Event Cameras Meet SPADs for High-Speed, Low-Bandwidth Imaging [25.13346470561497]
  Event cameras and single-photon avalanche diode (SPAD) sensors have emerged as promising alternatives to conventional cameras.<n>We show that these properties are complementary, and can help achieve low-light, high-speed image reconstruction with low bandwidth requirements.
  arXiv  Detail & Related papers  (2024-04-17T16:06:29Z)
• SpikeNVS: Enhancing Novel View Synthesis from Blurry Images via Spike Camera [78.20482568602993]
  Conventional RGB cameras are susceptible to motion blur. Neuromorphic cameras like event and spike cameras inherently capture more comprehensive temporal information. Our design can enhance novel view synthesis across NeRF and 3DGS.
  arXiv  Detail & Related papers  (2024-04-10T03:31:32Z)
• Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery [69.71080926778413]
  flare artifacts can affect image visual quality and downstream computer vision tasks. Current methods do not consider automatic exposure and tone mapping in image signal processing pipeline. We propose a solution to improve the performance of lens flare removal by revisiting the ISP and design a more reliable light sources recovery strategy.
  arXiv  Detail & Related papers  (2023-08-31T04:58:17Z)
• Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model [83.9497193551511]
  We introduce Lighting Every Darkness (LED), which is effective regardless of the digital gain or the camera sensor. LED eliminates the need for explicit noise model calibration, instead utilizing an implicit fine-tuning process that allows quick deployment and requires minimal data. LED also allows researchers to focus more on deep learning advancements while still utilizing sensor engineering benefits.
  arXiv  Detail & Related papers  (2023-08-07T10:09:11Z)
• Shakes on a Plane: Unsupervised Depth Estimation from Unstabilized Photography [54.36608424943729]
  We show that in a ''long-burst'', forty-two 12-megapixel RAW frames captured in a two-second sequence, there is enough parallax information from natural hand tremor alone to recover high-quality scene depth. We devise a test-time optimization approach that fits a neural RGB-D representation to long-burst data and simultaneously estimates scene depth and camera motion.
  arXiv  Detail & Related papers  (2022-12-22T18:54:34Z)
• Video super-resolution for single-photon LIDAR [0.0]
  3D Time-of-Flight (ToF) image sensors are used widely in applications such as self-driving cars, Augmented Reality (AR) and robotics. In this paper, we use synthetic depth sequences to train a 3D Convolutional Neural Network (CNN) for denoising and upscaling (x4) depth data. With GPU acceleration, frames are processed at >30 frames per second, making the approach suitable for low-latency imaging, as required for obstacle avoidance.
  arXiv  Detail & Related papers  (2022-10-19T11:33:29Z)
• Exploiting Raw Images for Real-Scene Super-Resolution [105.18021110372133]
  We study the problem of real-scene single image super-resolution to bridge the gap between synthetic data and real captured images. We propose a method to generate more realistic training data by mimicking the imaging process of digital cameras. We also develop a two-branch convolutional neural network to exploit the radiance information originally-recorded in raw images.
  arXiv  Detail & Related papers  (2021-02-02T16:10:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.