Pith. sign in

REVIEW 4 cited by

ReconFusion: 3D Reconstruction with Diffusion Priors

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2312.02981 v1 pith:TCUQ4FPI submitted 2023-12-05 cs.CV

Rundi Wu , Ben Mildenhall , Philipp Henzler , Keunhong Park , Ruiqi Gao , Daniel Watson , Pratul P. Srinivasan , Dor Verbin
show 3 more authors
Jonathan T. Barron Ben Poole Aleksander Holynski
This is my paper
classification cs.CV
keywords reconstructionnovelscenesdatasetsdiffusionimagesinputnerf
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes. However, recovering a high-quality NeRF typically requires tens to hundreds of input images, resulting in a time-consuming capture process. We present ReconFusion to reconstruct real-world scenes using only a few photos. Our approach leverages a diffusion prior for novel view synthesis, trained on synthetic and multiview datasets, which regularizes a NeRF-based 3D reconstruction pipeline at novel camera poses beyond those captured by the set of input images. Our method synthesizes realistic geometry and texture in underconstrained regions while preserving the appearance of observed regions. We perform an extensive evaluation across various real-world datasets, including forward-facing and 360-degree scenes, demonstrating significant performance improvements over previous few-view NeRF reconstruction approaches.

Discussion (0). Sign in to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. GenRecon: Bridging Generative Priors for Multi-View 3D Scene Reconstruction

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    GenRecon lifts object-level generative priors to scene-scale reconstruction by chunking scenes and using projection-based conditioning on multi-view features, claiming 16% better results than prior methods.

  2. MACRO: Training-free Multi-plane Attention for Closeup Render Optimization

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Training-free multi-plane attention with image-space scale-matched reference crops restores correct close-up detail from 3DGS without retraining the enhancer.

  3. KFC-W: Generating 3D-Consistent Videos from Unposed Internet Photos

    cs.CV 2024-11 unverdicted novelty 5.0 of 10

    KFC-W is a self-supervised 3D-aware video model trained on videos and multiview internet photos that produces geometrically consistent interpolations between unposed input images without any 3D annotations.

  4. TripoSR: Fast 3D Object Reconstruction from a Single Image

    cs.CV 2024-03 unverdicted novelty 5.0 of 10

    TripoSR generates 3D meshes from single images in under 0.5 seconds using an improved transformer architecture over LRM.

Pith tools