Accidental Light Probes
Hong-Xing Yu, Samir Agarwala, Charles Herrmann, Richard Szeliski, Noah Snavely, Jiajun Wu, Deqing Sun
Abstract
Recovering lighting in a scene from a single image is a fundamental problem in computer vision. While a mirror ball light probe can capture omnidirectional lighting, light probes are generally unavailable in everyday images. In this work, we study recovering lighting from accidental light probes (ALPs)-common, shiny objects like Coke cans, which often accidentally appear in daily scenes. We propose a physically-based approach to model ALPs and estimate lighting from their appearances in single images. The main idea is to model the appearance of ALPs by photogrammetrically principled shading and to invert this process via differentiable rendering to recover incidental illumination. We demonstrate that we can put an ALP into a scene to allow high-fidelity lighting estimation. Our model can also recover lighting for existing images that happen to contain an ALP * . I'd rather be Shiny. -Tamatoa from Moana, 2016
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Cited by top-tier papers6
- DiffusionLight: Light Probes for Free by Painting a Chrome BallPakkapon Phongthawee, Worameth Chinchuthakun, Nontaphat Sinsunthithet, Varun Jampani et al.CVPR 2024 · 21 citations
- LuxDiT: Lighting Estimation with Video Diffusion TransformerRuofan Liang, Kai He, Zan Gojcic, Igor Gilitschenski et al.NeurIPS 2025 · 20 citations
- OpenSubstance: A High-Quality Measured Dataset of Multi-View and -Lighting Images and ShapesFan Pei, Jinchen Bai, Xiang Feng, Zoubin Bi et al.ICCV 2025 · 3 citations
- Spatiotemporally Consistent Indoor Lighting Estimation with Diffusion PriorsMutian Tong, Rundi Wu, Changxi ZhengSIGGRAPH 2025 · 1 citation
- Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin DatasetZhao Dong, Ka Chen, Zhaoyang Lv, Hong-Xing Yu et al.CVPR 2025
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- Extracting Triangular 3D Models, Materials, and Lighting From ImagesJacob Munkberg, Wenzheng Chen, Jon Hasselgren, Alex Evans et al.CVPR 2022 · 306 citations
- Neural Inverse Rendering of an Indoor Scene From a Single ImageSoumyadip Sengupta, Jinwei Gu, Kihwan Kim, Guilin Liu et al.ICCV 2019 · 172 citations
- Deep Parametric Indoor Lighting EstimationMarc-André Gardner, Yannick Hold-Geoffroy, Kalyan Sunkavalli, Christian Gagné et al.ICCV 2019 · 155 citations
- Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural RenderingYuxuan Zhang, Wenzheng Chen, Huan Ling, Jun Gao et al.ICLR 2021 · 140 citations
- Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR ApplicationsSoo Ye Kim, Jihyong Oh, Munchurl KimICCV 2019 · 114 citations
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