New GAINS framework uses foundation models for sparse-view inverse rendering

Researchers have developed GAINS, a novel two-stage framework for inverse rendering that utilizes foundation models to improve material and geometry estimation from sparse multi-view captures. This approach stabilizes the process by integrating monocular depth, normal, and diffusion priors for geometry refinement, followed by segmentation, intrinsic image decomposition, and diffusion priors for material recovery. Experiments demonstrate that GAINS significantly enhances accuracy in material parameters, relighting, and novel-view synthesis, particularly in sparse-view scenarios where traditional methods struggle with ambiguity. AI

IMPACT This research could lead to more robust 3D reconstruction and material recovery from limited visual data, impacting fields like virtual reality and computer graphics.

RANK_REASON The cluster contains an academic paper detailing a new method for inverse rendering. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• diffusion priors
• foundation models
• GAINS
• Gaussian Splatting

• paper
• other

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