{"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/neural-pbir-reconstruction-of-shape-material","title":"Neural-PBIR Reconstruction of Shape, Material, and Illumination","arxiv_id":"2304.13445","date":"2023-04-26","proceeding":"ICCV 2023 1","authors":["Cheng Sun","Guangyan Cai","Zhengqin Li","Kai Yan","Cheng Zhang","Carl Marshall","Jia-Bin Huang","Shuang Zhao","Zhao Dong"],"abstract":"Reconstructing the shape and spatially varying surface appearances of a physical-world object as well as its surrounding illumination based on 2D images (e.g., photographs) of the object has been a long-standing problem in computer vision and graphics. In this paper, we introduce an accurate and highly efficient object reconstruction pipeline combining neural based object reconstruction and physics-based inverse rendering (PBIR). Our pipeline firstly leverages a neural SDF based shape reconstruction to produce high-quality but potentially imperfect object shape. Then, we introduce a neural material and lighting distillation stage to achieve high-quality predictions for material and illumination. In the last stage, initialized by the neural predictions, we perform PBIR to refine the initial results and obtain the final high-quality reconstruction of object shape, material, and illumination. Experimental results demonstrate our pipeline significantly outperforms existing methods quality-wise and performance-wise.","url_abs":"https://arxiv.org/abs/2304.13445v5","url_pdf":"https://arxiv.org/pdf/2304.13445v5.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":"object","task_name":"Object"},{"task_slug":"object-reconstruction","task_name":"Object Reconstruction"},{"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":"Neural-PBIR","rank_in_archive_order":1,"of":7,"metrics":{"HDR-PSNR":"26.01","LPIPS":"0.023","SSIM":"0.979"},"uses_additional_data":false},{"leaderboard":"/sota/inverse-rendering-on-stanford-orb","task":"Inverse Rendering","dataset":"Stanford-ORB","model":"Neural-PBIR","rank_in_archive_order":1,"of":7,"metrics":{"HDR-PSNR":"26.01"},"uses_additional_data":false},{"leaderboard":"/sota/surface-normals-estimation-on-stanford-orb","task":"Surface Normals Estimation","dataset":"Stanford-ORB","model":"Neural-PBIR","rank_in_archive_order":4,"of":7,"metrics":{"Cosine Distance":"0.06"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2304.13445","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}