Illumination Explorer: All-Frequency Illumination Estimation via HEALPix-Guided Diffusion

Abstract

Estimating panoramic illumination from a single limited-FOV input image is a critical yet challenging task for rendering realistic objects with complex materials in augmented reality. Existing methods typically either estimate parameterized lighting models or directly generate panoramas in an end-to-end manner. However, both approaches present significant challenges: 1) Parameterized methods struggle to simultaneously capture both high-frequency and low-frequency information under real lighting conditions, and lack a unified model for indoor and outdoor scenes. 2) Direct generation methods often produce unpredictable results, making it difficult to control the position, color, and structure of light sources in the output panorama. In this paper, we propose a unified illumination estimation method based on pretrained diffusion models guided by Hierarchical Equal Area isoLatitude Pixelization (HEALPix). We introduce HEALPix as a novel representation for panoramic illumination, providing a discrete and structured parameterization that supports uniform spherical sampling and retains high-frequency lighting variations. Based on this representation, we construct a conditional illumination diffusion model to generate out-of-view illumination content in a perceptually compressed LDR space. To support direct HDR output, we propose a reversible HDR compression strategy compatible with diffusion model training. Extensive experiments demonstrate that our Illumination Explorer generates HDR panoramas with high illumination accuracy and rich textural detail, outperforming previous methods in realistic composition for 3D objects with different reflective materials. Code is available at https://github.com/nauyihsnehs/IllumiExp