E3D: Event-Based 3D Shape Reconstruction

• URL: http://arxiv.org/abs/2012.05214v2
• Date: Thu, 10 Dec 2020 12:26:59 GMT
• Title: E3D: Event-Based 3D Shape Reconstruction
• Authors: Alexis Baudron, Zihao W. Wang, Oliver Cossairt and Aggelos K. Katsaggelos
• Abstract summary: 3D shape reconstruction is a primary component of augmented/virtual reality. Previous solutions based on RGB, RGB-D and Lidar sensors are power and data intensive. We approach 3D reconstruction with an event camera, a sensor with significantly lower power, latency and data expense.
• Score: 19.823758341937605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D shape reconstruction is a primary component of augmented/virtual reality. Despite being highly advanced, existing solutions based on RGB, RGB-D and Lidar sensors are power and data intensive, which introduces challenges for deployment in edge devices. We approach 3D reconstruction with an event camera, a sensor with significantly lower power, latency and data expense while enabling high dynamic range. While previous event-based 3D reconstruction methods are primarily based on stereo vision, we cast the problem as multi-view shape from silhouette using a monocular event camera. The output from a moving event camera is a sparse point set of space-time gradients, largely sketching scene/object edges and contours. We first introduce an event-to-silhouette (E2S) neural network module to transform a stack of event frames to the corresponding silhouettes, with additional neural branches for camera pose regression. Second, we introduce E3D, which employs a 3D differentiable renderer (PyTorch3D) to enforce cross-view 3D mesh consistency and fine-tune the E2S and pose network. Lastly, we introduce a 3D-to-events simulation pipeline and apply it to publicly available object datasets and generate synthetic event/silhouette training pairs for supervised learning.

Related papers

• E-3DGS: Gaussian Splatting with Exposure and Motion Events [29.042018288378447]
  We propose E-3DGS, a novel event-based approach that partitions events into motion and exposure. We introduce a novel integration of 3DGS with exposure events for high-quality reconstruction of explicit scene representations. Our method is faster and delivers better reconstruction quality than event-based NeRF while being more cost-effective than NeRF methods.
  arXiv  Detail & Related papers  (2024-10-22T13:17:20Z)
• EF-3DGS: Event-Aided Free-Trajectory 3D Gaussian Splatting [76.02450110026747]
  Event cameras, inspired by biological vision, record pixel-wise intensity changes asynchronously with high temporal resolution. We propose Event-Aided Free-Trajectory 3DGS, which seamlessly integrates the advantages of event cameras into 3DGS. We evaluate our method on the public Tanks and Temples benchmark and a newly collected real-world dataset, RealEv-DAVIS.
  arXiv  Detail & Related papers  (2024-10-20T13:44:24Z)
• IncEventGS: Pose-Free Gaussian Splatting from a Single Event Camera [7.515256982860307]
  IncEventGS is an incremental 3D Gaussian splatting reconstruction algorithm with a single event camera. We exploit the tracking and mapping paradigm of conventional SLAM pipelines for IncEventGS.
  arXiv  Detail & Related papers  (2024-10-10T16:54:23Z)
• Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion [54.197343533492486]
  Event3DGS can reconstruct high-fidelity 3D structure and appearance under high-speed egomotion. Experiments on multiple synthetic and real-world datasets demonstrate the superiority of Event3DGS compared with existing event-based dense 3D scene reconstruction frameworks. Our framework also allows one to incorporate a few motion-blurred frame-based measurements into the reconstruction process to further improve appearance fidelity without loss of structural accuracy.
  arXiv  Detail & Related papers  (2024-06-05T06:06:03Z)
• EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting [5.160735014509357]
  We propose the first event-based generalizable 3D reconstruction framework, called EvGGS. It reconstructs scenes as 3D Gaussians from only event input in a feedforward manner. Our approach performs better than all baselines in reconstruction quality, and depth/intensity predictions with satisfactory speed.
  arXiv  Detail & Related papers  (2024-05-23T18:10:26Z)
• EventEgo3D: 3D Human Motion Capture from Egocentric Event Streams [59.77837807004765]
  This paper introduces a new problem, i.e., 3D human motion capture from an egocentric monocular event camera with a fisheye lens. Event streams have high temporal resolution and provide reliable cues for 3D human motion capture under high-speed human motions and rapidly changing illumination. Our EE3D demonstrates robustness and superior 3D accuracy compared to existing solutions while supporting real-time 3D pose update rates of 140Hz.
  arXiv  Detail & Related papers  (2024-04-12T17:59:47Z)
• Denoising Diffusion via Image-Based Rendering [54.20828696348574]
  We introduce the first diffusion model able to perform fast, detailed reconstruction and generation of real-world 3D scenes. First, we introduce a new neural scene representation, IB-planes, that can efficiently and accurately represent large 3D scenes. Second, we propose a denoising-diffusion framework to learn a prior over this novel 3D scene representation, using only 2D images.
  arXiv  Detail & Related papers  (2024-02-05T19:00:45Z)
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm [114.47216525866435]
  We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation. For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
  arXiv  Detail & Related papers  (2023-10-12T17:59:57Z)
• EvAC3D: From Event-based Apparent Contours to 3D Models via Continuous Visual Hulls [46.94040300725127]
  3D reconstruction from multiple views is a successful computer vision field with multiple deployments in applications. We study the problem of 3D reconstruction from event-cameras, motivated by the advantages of event-based cameras in terms of low power and latency. We propose Apparent Contour Events (ACE), a novel event-based representation that defines the geometry of the apparent contour of an object.
  arXiv  Detail & Related papers  (2023-04-11T15:46:16Z)
• 3D-to-2D Distillation for Indoor Scene Parsing [78.36781565047656]
  We present a new approach that enables us to leverage 3D features extracted from large-scale 3D data repository to enhance 2D features extracted from RGB images. First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training. Second, we design a two-stage dimension normalization scheme to calibrate the 2D and 3D features for better integration. Third, we design a semantic-aware adversarial training model to extend our framework for training with unpaired 3D data.
  arXiv  Detail & Related papers  (2021-04-06T02:22:24Z)
• CubifAE-3D: Monocular Camera Space Cubification for Auto-Encoder based 3D Object Detection [8.134961550216618]
  We introduce a method for 3D object detection using a single monocular image. We show that we can pre-train the AE using paired RGB and depth images from simulation data once and subsequently only train the 3DOD network using real data. Our 3DOD network utilizes a particular cubification' of 3D space around the camera, where each cuboid is tasked with predicting N object poses, along with their class and confidence values.
  arXiv  Detail & Related papers  (2020-06-07T08:17:00Z)

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.