A Plug-and-Play Algorithm for 3D Video Super-Resolution of Single-Photon LiDAR data

• URL: http://arxiv.org/abs/2412.09427v1
• Date: Thu, 12 Dec 2024 16:33:06 GMT
• Title: A Plug-and-Play Algorithm for 3D Video Super-Resolution of Single-Photon LiDAR data
• Authors: Alice Ruget, Lewis Wilson, Jonathan Leach, Rachael Tobin, Aongus Mccarthy, Gerald S. Buller, Steve Mclaughlin, Abderrahim Halimi,
• Abstract summary: Single-photon avalanche diodes (SPADs) are advanced sensors capable of detecting individual photons and recording their arrival times with picosecond resolution. We propose a novel computational imaging algorithm to improve the 3D reconstruction of moving scenes from SPAD data.
• Score: 5.378429123269604
• License:
• Abstract: Single-photon avalanche diodes (SPADs) are advanced sensors capable of detecting individual photons and recording their arrival times with picosecond resolution using time-correlated Single-Photon Counting detection techniques. They are used in various applications, such as LiDAR, and can capture high-speed sequences of binary single-photon images, offering great potential for reconstructing 3D environments with high motion dynamics. To complement single-photon data, they are often paired with conventional passive cameras, which capture high-resolution (HR) intensity images at a lower frame rate. However, 3D reconstruction from SPAD data faces challenges. Aggregating multiple binary measurements improves precision and reduces noise but can cause motion blur in dynamic scenes. Additionally, SPAD arrays often have lower resolution than passive cameras. To address these issues, we propose a novel computational imaging algorithm to improve the 3D reconstruction of moving scenes from SPAD data by addressing the motion blur and increasing the native spatial resolution. We adopt a plug-and-play approach within an optimization scheme alternating between guided video super-resolution of the 3D scene, and precise image realignment using optical flow. Experiments on synthetic data show significantly improved image resolutions across various signal-to-noise ratios and photon levels. We validate our method using real-world SPAD measurements on three practical situations with dynamic objects. First on fast-moving scenes in laboratory conditions at short range; second very low resolution imaging of people with a consumer-grade SPAD sensor from STMicroelectronics; and finally, HR imaging of people walking outdoors in daylight at a range of 325 meters under eye-safe illumination conditions using a short-wave infrared SPAD camera. These results demonstrate the robustness and versatility of our approach.

Related papers

• SweepEvGS: Event-Based 3D Gaussian Splatting for Macro and Micro Radiance Field Rendering from a Single Sweep [48.34647667445792]
  SweepEvGS is a novel hardware-integrated method that leverages event cameras for robust and accurate novel view synthesis from a single sweep. We validate the robustness and efficiency of SweepEvGS through experiments in three different imaging settings. Our results demonstrate that SweepEvGS surpasses existing methods in visual rendering quality, rendering speed, and computational efficiency.
  arXiv  Detail & Related papers  (2024-12-16T09:09:42Z)
• 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)
• Towards 3D Vision with Low-Cost Single-Photon Cameras [24.711165102559438]
  We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature, energy-efficient, low-cost single-photon cameras. Our work draws a connection between image-based modeling and active range scanning and is a step towards 3D vision with single-photon cameras.
  arXiv  Detail & Related papers  (2024-03-26T15:40:05Z)
• StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D [88.66678730537777]
  We present StableDreamer, a methodology incorporating three advances. First, we formalize the equivalence of the SDS generative prior and a simple supervised L2 reconstruction loss. Second, our analysis shows that while image-space diffusion contributes to geometric precision, latent-space diffusion is crucial for vivid color rendition.
  arXiv  Detail & Related papers  (2023-12-02T02:27:58Z)
• 3D Data Augmentation for Driving Scenes on Camera [50.41413053812315]
  We propose a 3D data augmentation approach termed Drive-3DAug, aiming at augmenting the driving scenes on camera in the 3D space. We first utilize Neural Radiance Field (NeRF) to reconstruct the 3D models of background and foreground objects. Then, augmented driving scenes can be obtained by placing the 3D objects with adapted location and orientation at the pre-defined valid region of backgrounds.
  arXiv  Detail & Related papers  (2023-03-18T05:51:05Z)
• 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)
• Single-Photon Structured Light [31.614032717665832]
  "Single-Photon Structured Light" works by sensing binary images that indicates the presence or absence of photon arrivals during each exposure. We develop novel temporal sequences using error correction codes that are designed to be robust to short-range effects like projector and camera defocus. Our lab prototype is capable of 3D imaging in challenging scenarios involving objects with extremely low albedo or undergoing fast motion.
  arXiv  Detail & Related papers  (2022-04-11T17:57:04Z)
• High-speed object detection with a single-photon time-of-flight image sensor [2.648554238948439]
  We present results from a portable SPAD camera system that outputs 16-bin photon timing histograms with 64x32 spatial resolution. The results are relevant for safety-critical computer vision applications which would benefit from faster than human reaction times.
  arXiv  Detail & Related papers  (2021-07-28T14:53:44Z)
• Quanta Burst Photography [15.722085082004934]
  Single-photon avalanche diodes (SPADs) are an emerging sensor technology capable of detecting individual incident photons. We present quanta burst photography, a computational photography technique that leverages SPCs as passive imaging devices for photography in challenging conditions.
  arXiv  Detail & Related papers  (2020-06-21T16:20:29Z)
• Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation [57.11299763566534]
  We present a solution to recover 3D pose from multi-view images captured with spatially calibrated cameras. We exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points. Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections.
  arXiv  Detail & Related papers  (2020-04-05T12:52:29Z)

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.