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ReconFusion: 3D Reconstruction with Diffusion Priors
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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.
Forward citations
Cited by 4 Pith papers
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Wonderland: Navigating 3D Scenes from a Single Image
A feed-forward pipeline reconstructs 3D Gaussian scenes from single images by regressing 3DGS directly from camera-conditioned video diffusion latents.
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MACRO: Training-free Multi-plane Attention for Closeup Render Optimization
Training-free multi-plane attention with image-space scale-matched reference crops restores correct close-up detail from 3DGS without retraining the enhancer.
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Sparfels: Fast Reconstruction from Sparse Unposed Imagery
Sparfels reconstructs accurate 3D shape and novel views from sparse, unposed images in minutes by bootstrapping 2D Gaussian splatting with MASt3R and a new splatted-color-variance loss.
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MutualNeRF: Improve the Performance of NeRF under Limited Samples with Mutual Information Theory
A mutual information framing for NeRF that selects low-redundancy views and adds CLIP and color regularization, reporting small PSNR gains in few-shot settings.
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