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.

Estimating lighting in indoor scenes is particularly challenging due to diverse distribution of light sources and complexity of scene geometry. Previous methods mainly focused on spatial variability and consistency for a single image or temporal consistency for video sequences. However, these approaches fail to achieve spatio-temporal consistency in video lighting estimation, which restricts applications such as compositing animated models into videos. In this paper, we propose STGlight, a lightweight and effective method for spatio-temporally consistent video lighting estimation, where our network processes a stream of LDR RGB-D video frames while maintaining incrementally updated global representations of both geometry and lighting, enabling the prediction of HDR environment maps at arbitrary locations for each frame. We model indoor lighting with three components: visible light sources providing direct illumination, ambient lighting approximating indirect illumination, and local environment textures producing high-quality specular reflections on glossy objects. To capture spatial-varying lighting, we represent scene geometry with point clouds, which support efficient spatio-temporal fusion and allow us to handle moderately dynamic scenes. To ensure temporal consistency, we apply a transformer-based fusion block that propagates lighting features across frames. Building on this, we further handle dynamic lighting with moving objects or changing light conditions by applying intrinsic decomposition on the point cloud and integrating the decomposed components with a neural fusion module. Experiments show that our online method can effectively predict lighting for any position within the video stream, while maintaining spatial variability and spatio-temporal consistency. Code is available at: https://github.com/nauyihsnehs/STGlight.

Illumination estimation from a single indoor image is a promising yet challenging task. Existing indoor illumination estimation methods mainly regress lighting parameters or infer a panorama from a limited field-of-view image. Nevertheless, these methods fail to recover a panorama with both well-distributed illumination and detailed environment textures, leading to a lack of realism in rendering the embedded 3D objects with complex materials. This paper presents a novel multi-stage illumination estimation framework named IllumiDiff. Specifically, in Stage I, we first estimate illumination conditions from the input image, including the illumination distribution as well as the environmental texture of the scene. In Stage II, guided by the estimated illumination conditions, we design a conditional panoramic texture diffusion model to generate a high-quality LDR panorama. In Stage III, we leverage the illumination conditions to further reconstruct the LDR panorama to an HDR panorama. Extensive experiments demonstrate that our IllumiDiff can generate an HDR panorama with realistic illumination distribution and rich texture details from a single limited field-of-view indoor image. The generated panorama can produce impressive rendering results for the embedded 3D objects with various materials.