arXiv Computer Vision By Zheng Gu, Rui Huang, Xilu Zhang, Jingbo Zhang, Min Lu, Zhida Sun, Dani Lischinski, Daniel Cohen-Or, Hui Huang

IntrinSync: Joint Intrinsic Decomposition and Reciprocal Rendering

Read the original on arXiv Computer Vision →

IntrinSync is a unified framework that jointly decomposes an RGB image into albedo, shading, surface normal, roughness, and metallic maps through a 1-to-N mapping, allowing information exchange across channels. It also establishes inverse-forward reciprocity via a dual cycle-consistent objective that aligns predictions across a closed loop, improving coherence and physical consistency. The method demonstrates competitive performance on three datasets and offers a physically grounded interface for image editing by manipulating intrinsic properties and rendering them back into RGB images.

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arXiv Computer Vision
Sep 24

GLOW: Global Illumination-Aware Inverse Rendering of Indoor Scenes Captured with Dynamic Co-Located Light & Camera

GLOW is a Global Illumination‑aware inverse rendering framework for indoor scenes captured with dynamic co‑located light and camera setups. It combines a neural implicit surface representation with a neural radiance cache to jointly optimize geometry and reflectance, while introducing a dynamic radiance cache and a surface‑angle‑weighted radiometric loss to handle near‑field motion, strong inter‑reflections, and specular highlights. Experiments demonstrate that GLOW significantly outperforms prior methods in estimating material reflectance under both natural and co‑located illumination.

By Jiaye Wu, Saeed Hadadan, Geng Lin, Peihan Tu, Auguste Gezalyan, Matthias Zwicker, David Jacobs, Roni Sengupta
arXiv Computer Vision
Sep 18

GS-PI: An Optimization-Decoupled Appearance Decomposition Approach for Generating PBR Gaussian Assets

GS-PI introduces an optimization‑decoupled framework that transforms Gaussian Splatting (GS) assets into physically based rendering (PBR) compatible Gaussian assets. By treating PBR material generation as a geometry‑conditioned diffusion process on 3D point clouds, it achieves multi‑view consistency and avoids the pixel‑correspondence problems of 2D diffusion. The method employs a multi‑scale cross‑view conditioning mechanism—combining global semantic priors, photometric cues, and spatial view‑direction signals—to prevent specular highlights from baking into intrinsic colors, and then distills the predicted attributes back into a fully relightable PBR‑GS asset without requiring proxy meshes.

By Jieting Xu, Rengan Xie, Zijian Huang, Zehui Jin, Rui Wang, Yuchi Huo
Hugging Face Trending Papers
Aug 6

Floating Radiance Networks

Recent advances in neural scene representations enable photorealistic novel-view synthesis, yet most methods remain tightly coupled to a single rendering paradigm, limiting their versatility and integration with conventional graphics workflows. We introduce Floating Radiance Networks (FlaRe), a neural scene representation combining explicit ray-traceable geometry with continuous neural radiance functions.