GAINS: Gaussian-based Inverse Rendering from Sparse Multi-View Captures

• URL: http://arxiv.org/abs/2512.09925v1
• Date: Wed, 10 Dec 2025 18:58:11 GMT
• Title: GAINS: Gaussian-based Inverse Rendering from Sparse Multi-View Captures
• Authors: Patrick Noras, Jun Myeong Choi, Didier Stricker, Pieter Peers, Roni Sengupta,
• Abstract summary: We introduce GAINS (Gaussian-based Inverse rendering from Sparse multi-view captures)<n>It is a two-stage inverse rendering framework that leverages learning-based priors to stabilize geometry and material estimation.<n>It significantly improves material parameter accuracy, relighting quality, and novel-view synthesis compared to state-of-the-art inverse rendering methods.
• Score: 23.677487511754006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Gaussian Splatting-based inverse rendering extend Gaussian primitives with shading parameters and physically grounded light transport, enabling high-quality material recovery from dense multi-view captures. However, these methods degrade sharply under sparse-view settings, where limited observations lead to severe ambiguity between geometry, reflectance, and lighting. We introduce GAINS (Gaussian-based Inverse rendering from Sparse multi-view captures), a two-stage inverse rendering framework that leverages learning-based priors to stabilize geometry and material estimation. GAINS first refines geometry using monocular depth/normal and diffusion priors, then employs segmentation, intrinsic image decomposition (IID), and diffusion priors to regularize material recovery. Extensive experiments on synthetic and real-world datasets show that GAINS significantly improves material parameter accuracy, relighting quality, and novel-view synthesis compared to state-of-the-art Gaussian-based inverse rendering methods, especially under sparse-view settings. Project page: https://patrickbail.github.io/gains/

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