PRISM: Feed-Forward Single-Image 3D Reconstruction via Geometric Warp-Residual Modeling

• URL: http://arxiv.org/abs/2606.25430v1
• Date: Wed, 24 Jun 2026 05:48:20 GMT
• Title: PRISM: Feed-Forward Single-Image 3D Reconstruction via Geometric Warp-Residual Modeling
• Abstract summary: Reconstructing 3D scenes from a single image is a fundamental challenge in computer vision.<n>Recent methods achieve outstanding performance by leveraging camera-controlled video diffusion models.<n>We propose PRISM, a feed-forward framework that decomposes multi-view latent prediction into a parameter-free geometric prior.
• Score: 11.124244013253806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstructing 3D scenes from a single image is a fundamental challenge in computer vision, with broad applications in virtual reality, robotics, and content creation. Recent methods achieve outstanding performance by leveraging camera-controlled video diffusion models, but rely on iterative diffusion sampling, which greatly limits their practical deployment. We observe that geometric forward warping alone can cover the majority of a target view directly from the input image, with only a compact residual left for the encoder to correct. Motivated by this observation, we propose PRISM, a feed-forward framework that decomposes multi-view latent prediction into a parameter-free geometric prior and a learned residual correction, with no diffusion sampling required at inference. To enable generalization from purely synthetic training data, we devise a two-stage training strategy combining latents supervised distillation for geometric generalization and perceptual fine-tuning for appearance quality optimization. Extensive experiments on three benchmarks demonstrate that PRISM achieves competitive reconstruction quality compared with diffusion-based methods, while reducing inference time dramatically to only 36 seconds per scene.

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