{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/physg-inverse-rendering-with-spherical","title":"PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting","arxiv_id":"2104.00674","date":"2021-04-01","proceeding":"CVPR 2021 1","authors":["Kai Zhang","Fujun Luan","Qianqian Wang","Kavita Bala","Noah Snavely"],"abstract":"We present PhySG, an end-to-end inverse rendering pipeline that includes a fully differentiable renderer and can reconstruct geometry, materials, and illumination from scratch from a set of RGB input images. Our framework represents specular BRDFs and environmental illumination using mixtures of spherical Gaussians, and represents geometry as a signed distance function parameterized as a Multi-Layer Perceptron. The use of spherical Gaussians allows us to efficiently solve for approximate light transport, and our method works on scenes with challenging non-Lambertian reflectance captured under natural, static illumination. We demonstrate, with both synthetic and real data, that our reconstructions not only enable rendering of novel viewpoints, but also physics-based appearance editing of materials and illumination.","url_abs":"https://arxiv.org/abs/2104.00674v1","url_pdf":"https://arxiv.org/pdf/2104.00674v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"depth-prediction","task_name":"Depth Prediction"},{"task_slug":"image-relighting","task_name":"Image Relighting"},{"task_slug":"inverse-rendering","task_name":"Inverse Rendering"},{"task_slug":"surface-normals-estimation","task_name":"Surface Normals Estimation"},{"task_slug":"surface-reconstruction","task_name":"Surface Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-relighting-on-stanford-orb","task":"Image Relighting","dataset":"Stanford-ORB","model":"PhySG","rank_in_archive_order":7,"of":7,"metrics":{"HDR-PSNR":"21.81","LPIPS":"0.055","SSIM":"0.960"},"uses_additional_data":false},{"leaderboard":"/sota/inverse-rendering-on-stanford-orb","task":"Inverse Rendering","dataset":"Stanford-ORB","model":"PhySG","rank_in_archive_order":7,"of":7,"metrics":{"HDR-PSNR":"21.81"},"uses_additional_data":false},{"leaderboard":"/sota/surface-normals-estimation-on-stanford-orb","task":"Surface Normals Estimation","dataset":"Stanford-ORB","model":"PhySG","rank_in_archive_order":5,"of":7,"metrics":{"Cosine Distance":"0.17"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2104.00674","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}