3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surfaces

Linyi Jin, Nilesh Kulkarni, David F. Fouhey

Research output: Contribution to journalConference articlepeer-review

Abstract

This paper introduces 3DFIRES, a novel system for scene-level 3D reconstruction from posed images. Designed to work with as few as one view, 3DFIRES reconstructs the complete geometry of unseen scenes, including hidden surfaces. With multiple view inputs, our method pro-duces full reconstruction within all camera frustums. A key feature of our approach is the fusion of multi-view information at the feature level, enabling the production of coherent and comprehensive 3D reconstruction. We train our system on non-watertight scans from large-scale real scene dataset. We show it matches the efficacy of single-view reconstruction methods with only one input and surpasses existing techniques in both quantitative and qualitative measures for sparse-view 3D reconstruction. Project page: https://jinlinyi.github.io/3DFIRES/

Original languageEnglish (US)
Pages (from-to)9742-9751
Number of pages10
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs
StatePublished - 2024
Event2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 - Seattle, United States
Duration: Jun 16 2024Jun 22 2024

Keywords

  • 3D vision
  • hidden surfaces
  • scene reconstruction

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of '3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surfaces'. Together they form a unique fingerprint.

Cite this