@inproceedings{34383fe4fff84a0fa3dd3dc940433c7f,
title = "LightOctree: Lightweight 3D Spatially-Coherent Indoor Lighting Estimation",
abstract = "We present a lightweight solution for estimating spatially-coherent indoor lighting from a single RGB image. Previous methods for estimating illumination using volumetric repre-sentations have overlooked the sparse distribution of light sources in space, necessitating substantial memory and computational resources for achieving high-quality results. We introduce a unified, voxel octree-based illumination estimation framework to produce 3D spatially-coherent lighting. Additionally, a differentiable voxel octree cone tracing ren-dering layer is proposed to eliminate regular volumetric representation throughout the entire process and ensure the retention of features across different frequency domains. This reduction significantly decreases spatial usage and required floating-point operations without substantially compromising precision. Experimental results demonstrate that our approach achieves high-quality coherent estimation with minimal cost compared to previous methods.",
keywords = "indoor scene, lighting estimation, lightweight, spatially-coherent",
author = "Xuecan Wang and Shibang Xiao and Xiaohui Liang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 ; Conference date: 16-06-2024 Through 22-06-2024",
year = "2024",
doi = "10.1109/CVPR52733.2024.00434",
language = "英语",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
publisher = "IEEE Computer Society",
pages = "4536--4545",
booktitle = "Proceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024",
address = "美国",
}