EvAC3D: From Event-based Apparent Contours to 3D Models via Continuous Visual Hulls

• URL: http://arxiv.org/abs/2304.05296v1
• Date: Tue, 11 Apr 2023 15:46:16 GMT
• Title: EvAC3D: From Event-based Apparent Contours to 3D Models via Continuous Visual Hulls
• Authors: Ziyun Wang, Kenneth Chaney, Kostas Daniilidis
• Abstract summary: 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.
• Score: 46.94040300725127
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 3D reconstruction from multiple views is a successful computer vision field with multiple deployments in applications. State of the art is based on traditional RGB frames that enable optimization of photo-consistency cross views. In this paper, 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 as well as by the biological evidence that eyes in nature capture the same data and still perceive well 3D shape. The foundation of our hypothesis that 3D reconstruction is feasible using events lies in the information contained in the occluding contours and in the continuous scene acquisition with events. We propose Apparent Contour Events (ACE), a novel event-based representation that defines the geometry of the apparent contour of an object. We represent ACE by a spatially and temporally continuous implicit function defined in the event x-y-t space. Furthermore, we design a novel continuous Voxel Carving algorithm enabled by the high temporal resolution of the Apparent Contour Events. To evaluate the performance of the method, we collect MOEC-3D, a 3D event dataset of a set of common real-world objects. We demonstrate the ability of EvAC3D to reconstruct high-fidelity mesh surfaces from real event sequences while allowing the refinement of the 3D reconstruction for each individual event.

Related papers

• 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)
• Zero-Shot Multi-Object Shape Completion [59.325611678171974]
  We present a 3D shape completion method that recovers the complete geometry of multiple objects in complex scenes from a single RGB-D image. Our method outperforms the current state-of-the-art on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-03-21T17:59:59Z)
• Object-Centric Domain Randomization for 3D Shape Reconstruction in the Wild [22.82439286651921]
  One of the biggest challenges in single-view 3D shape reconstruction in the wild is the scarcity of 3D shape, 2D image>-paired data from real-world environments. Inspired by remarkable achievements via domain randomization, we propose ObjectDR which synthesizes such paired data via a random simulation of visual variations in object appearances and backgrounds.
  arXiv  Detail & Related papers  (2024-03-21T16:40:10Z)
• 3D-SceneDreamer: Text-Driven 3D-Consistent Scene Generation [51.64796781728106]
  We propose a generative refinement network to synthesize new contents with higher quality by exploiting the natural image prior to 2D diffusion model and the global 3D information of the current scene. Our approach supports wide variety of scene generation and arbitrary camera trajectories with improved visual quality and 3D consistency.
  arXiv  Detail & Related papers  (2024-03-14T14:31:22Z)
• Rethinking Event-based Human Pose Estimation with 3D Event Representations [26.592295349210787]
  Event cameras offer a robust solution for navigating challenging contexts. We introduce two 3D event representations: the Rasterized Event Point Cloud and the Decoupled Event Voxel. Experiments on EV-3DPW demonstrate that the robustness of our proposed 3D representation methods compared to traditional RGB images and event frame techniques.
  arXiv  Detail & Related papers  (2023-11-08T10:45:09Z)
• Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image [22.037472446683765]
  We learn a regular grid of 3D voxel features from the input image which is aligned with 3D scene space via a 3D feature lifting operator. Based on the 3D voxel features, our novel CenterNet-3D detection head formulates the 3D detection as keypoint detection in the 3D space. We devise an efficient coarse-to-fine reconstruction module, including coarse-level voxelization and a novel local PCA-SDF shape representation.
  arXiv  Detail & Related papers  (2021-11-04T18:30:37Z)
• Learning Canonical 3D Object Representation for Fine-Grained Recognition [77.33501114409036]
  We propose a novel framework for fine-grained object recognition that learns to recover object variation in 3D space from a single image. We represent an object as a composition of 3D shape and its appearance, while eliminating the effect of camera viewpoint. By incorporating 3D shape and appearance jointly in a deep representation, our method learns the discriminative representation of the object.
  arXiv  Detail & Related papers  (2021-08-10T12:19:34Z)
• Weakly Supervised Learning of Multi-Object 3D Scene Decompositions Using Deep Shape Priors [69.02332607843569]
  PriSMONet is a novel approach for learning Multi-Object 3D scene decomposition and representations from single images. A recurrent encoder regresses a latent representation of 3D shape, pose and texture of each object from an input RGB image. We evaluate the accuracy of our model in inferring 3D scene layout, demonstrate its generative capabilities, assess its generalization to real images, and point out benefits of the learned representation.
  arXiv  Detail & Related papers  (2020-10-08T14:49:23Z)

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.