{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T16:14:47Z","timestamp":1756311287828},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>Recovering the shape and appearance of real-world objects from natural 2D images is a long-standing and challenging inverse rendering problem. In this paper, we introduce a novel hybrid differentiable rendering method to efficiently reconstruct the 3D geometry and reflectance of a scene from multi-view images captured by conventional hand-held cameras. Our method follows an analysis-by-synthesis approach and consists of two phases. In the initialization phase, we use traditional SfM and MVS methods to reconstruct a virtual scene roughly matching the real scene. Then in the optimization phase, we adopt a hybrid approach to refine the geometry and reflectance, where the geometry is first optimized using an approximate differentiable rendering method, and the reflectance is optimized afterward using a physically-based differentiable rendering method. Our hybrid approach combines the efficiency of approximate methods with the high-quality results of physically-based methods. Extensive experiments on synthetic and real data demonstrate that our method can produce reconstructions with similar or higher quality than state-of-the-art methods while being more efficient.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/205","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T04:31:30Z","timestamp":1691728290000},"page":"1849-1857","source":"Crossref","is-referenced-by-count":1,"title":["Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method"],"prefix":"10.24963","author":[{"given":"Xiangyang","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Software, Tsinghua University, China"},{"name":"Beijing National Research Center for Information Science and Technology (BNRist), China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiling","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, China"},{"name":"Beijing National Research Center for Information Science and Technology (BNRist), China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bailin","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, Cardiff University, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, China"},{"name":"Beijing National Research Center for Information Science and Technology (BNRist), China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2023","name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","start":{"date-parts":[[2023,8,19]]},"theme":"Artificial Intelligence","location":"Macau, SAR China","end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T04:41:00Z","timestamp":1691728860000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/205"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/205","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}