Sparse Needlets for Lighting Estimation with Spherical Transport Loss

TL;DR

NeedleLight introduces a novel lighting estimation method using sparse needlets and spherical transport loss, effectively capturing complex illumination in both frequency and spatial domains, outperforming existing approaches.

Contribution

The paper proposes NeedleLight, a lighting estimation model that combines needlet-based representation with a spherical transport loss for improved accuracy and localization.

Findings

Outperforms state-of-the-art methods in lighting estimation

Achieves superior localization of illumination parameters

Demonstrates robustness across multiple evaluation metrics

Abstract

Accurate lighting estimation is challenging yet critical to many computer vision and computer graphics tasks such as high-dynamic-range (HDR) relighting. Existing approaches model lighting in either frequency domain or spatial domain which is insufficient to represent the complex lighting conditions in scenes and tends to produce inaccurate estimation. This paper presents NeedleLight, a new lighting estimation model that represents illumination with needlets and allows lighting estimation in both frequency domain and spatial domain jointly. An optimal thresholding function is designed to achieve sparse needlets which trims redundant lighting parameters and demonstrates superior localization properties for illumination representation. In addition, a novel spherical transport loss is designed based on optimal transport theory which guides to regress lighting representation parameters with consideration of the spatial information. Furthermore, we propose a new metric that is concise yet effective by directly evaluating the estimated illumination maps rather than rendered images. Extensive experiments show that NeedleLight achieves superior lighting estimation consistently across multiple evaluation metrics as compared with state-of-the-art methods.