mlsanityData to 7 Oct 2026

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

arXiv 2403.08768 ↗First posted 13 Mar 2024cs.CVcited by 6

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Abstract (arXiv)

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 produces 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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 40 of its 49 references that are in the map. First posted on arXiv 13 Mar 2024.

Builds on

40 of 49 references.

References · 40

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28 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 3.

6 papers.

  1. 2025Sparse-View 3D Reconstruction: Recent Advances and Open Challengescited by 3
  2. 2024TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene Understandingcited by 2
  3. 2025RayletDF: Raylet Distance Fields for Generalizable 3D Surface Reconstruction from Point Clouds or Gaussianscited by 1
  4. 2026R2S-EGO: Dual-Proxy Refinement for Sparse-Capture Real-to-Simcited by 1
  5. 2026Timestep Rescheduling in Diffusion Inversioncited by 0
  6. 2026GaussianDS: Depth-supervised Semantic Gaussian Splatting for Scene Understandingcited by 0