March 26, 2024, 4:49 a.m. | Hoon-Gyu Chung, Seokjun Choi, Seung-Hwan Baek

cs.CV updates on arXiv.org arxiv.org

arXiv:2312.02480v2 Announce Type: replace
Abstract: We present differentiable point-based inverse rendering, DPIR, an analysis-by-synthesis method that processes images captured under diverse illuminations to estimate shape and spatially-varying BRDF. To this end, we adopt point-based rendering, eliminating the need for multiple samplings per ray, typical of volumetric rendering, thus significantly enhancing the speed of inverse rendering. To realize this idea, we devise a hybrid point-volumetric representation for geometry and a regularized basis-BRDF representation for reflectance. The hybrid geometric representation enables fast …

abstract analysis arxiv cs.cv differentiable diverse images inverse rendering multiple per processes ray rendering speed synthesis type

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Business Data Analyst

@ Alstom | Johannesburg, GT, ZA