Virtual Correspondence: Humans as a Cue for Extreme-View Geometry

• URL: http://arxiv.org/abs/2206.08365v1
• Date: Thu, 16 Jun 2022 17:59:42 GMT
• Title: Virtual Correspondence: Humans as a Cue for Extreme-View Geometry
• Authors: Wei-Chiu Ma, Anqi Joyce Yang, Shenlong Wang, Raquel Urtasun, Antonio Torralba
• Abstract summary: We present a novel concept called virtual correspondences (VCs) VCs conform with epipolar geometry; unlike classic correspondences, VCs do not need to be co-visible across views. We show how VCs can be seamlessly integrated with classic bundle adjustment to recover camera poses across extreme views.
• Score: 104.09449367670318
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recovering the spatial layout of the cameras and the geometry of the scene from extreme-view images is a longstanding challenge in computer vision. Prevailing 3D reconstruction algorithms often adopt the image matching paradigm and presume that a portion of the scene is co-visible across images, yielding poor performance when there is little overlap among inputs. In contrast, humans can associate visible parts in one image to the corresponding invisible components in another image via prior knowledge of the shapes. Inspired by this fact, we present a novel concept called virtual correspondences (VCs). VCs are a pair of pixels from two images whose camera rays intersect in 3D. Similar to classic correspondences, VCs conform with epipolar geometry; unlike classic correspondences, VCs do not need to be co-visible across views. Therefore VCs can be established and exploited even if images do not overlap. We introduce a method to find virtual correspondences based on humans in the scene. We showcase how VCs can be seamlessly integrated with classic bundle adjustment to recover camera poses across extreme views. Experiments show that our method significantly outperforms state-of-the-art camera pose estimation methods in challenging scenarios and is comparable in the traditional densely captured setup. Our approach also unleashes the potential of multiple downstream tasks such as scene reconstruction from multi-view stereo and novel view synthesis in extreme-view scenarios.

Related papers

• State of the Art in Dense Monocular Non-Rigid 3D Reconstruction [100.9586977875698]
  3D reconstruction of deformable (or non-rigid) scenes from a set of monocular 2D image observations is a long-standing and actively researched area of computer vision and graphics. This survey focuses on state-of-the-art methods for dense non-rigid 3D reconstruction of various deformable objects and composite scenes from monocular videos or sets of monocular views.
  arXiv  Detail & Related papers  (2022-10-27T17:59:53Z)
• EpipolarNVS: leveraging on Epipolar geometry for single-image Novel View Synthesis [6.103988053817792]
  Novel-view synthesis (NVS) can be tackled through different approaches, depending on the general setting. The most challenging scenario, the one where we stand in this work, only considers a unique source image to generate a novel one from another viewpoint. We introduce an innovative method that encodes the viewpoint transformation as a 2D feature image.
  arXiv  Detail & Related papers  (2022-10-24T09:54:20Z)
• S$^3$-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint [22.42916940712357]
  Our method learns a neural reflectance field to represent the 3D geometry and BRDFs of a scene. Our method is capable of recovering 3D geometry, including both visible and invisible parts, of a scene from single-view images. It supports applications like novel-view synthesis and relighting.
  arXiv  Detail & Related papers  (2022-10-17T11:01:52Z)
• Neural Groundplans: Persistent Neural Scene Representations from a Single Image [90.04272671464238]
  We present a method to map 2D image observations of a scene to a persistent 3D scene representation. We propose conditional neural groundplans as persistent and memory-efficient scene representations.
  arXiv  Detail & Related papers  (2022-07-22T17:41:24Z)
• Recognizing Scenes from Novel Viewpoints [99.90914180489456]
  Humans can perceive scenes in 3D from a handful of 2D views. For AI agents, the ability to recognize a scene from any viewpoint given only a few images enables them to efficiently interact with the scene and its objects. We propose a model which takes as input a few RGB images of a new scene and recognizes the scene from novel viewpoints by segmenting it into semantic categories.
  arXiv  Detail & Related papers  (2021-12-02T18:59:40Z)
• IBRNet: Learning Multi-View Image-Based Rendering [67.15887251196894]
  We present a method that synthesizes novel views of complex scenes by interpolating a sparse set of nearby views. By drawing on source views at render time, our method hearkens back to classic work on image-based rendering.
  arXiv  Detail & Related papers  (2021-02-25T18:56:21Z)
• 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.