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Sloan Foundation","doi-asserted-by":"publisher","award":["FG202013153"],"award-info":[{"award-number":["FG202013153"]}],"id":[{"id":"10.13039\/100000879","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2024,12,19]]},"abstract":"<jats:p>We introduce a method for high-quality 3D reconstruction from multi-view images. Our method uses a new point-based representation, the regularized dipole sum, which generalizes the winding number to allow for interpolation of per-point attributes in point clouds with noisy or outlier points. Using regularized dipole sums, we represent implicit geometry and radiance fields as per-point attributes of a dense point cloud, which we initialize from structure from motion. We additionally derive Barnes-Hut fast summation schemes for accelerated forward and adjoint dipole sum queries. These queries facilitate the use of ray tracing to efficiently and differentiably render images with our point-based representations, and thus update their point attributes to optimize scene geometry and appearance. We evaluate our method in inverse rendering applications against state-of-the-art alternatives, based on ray tracing of neural representations or rasterization of Gaussian point-based representations. Our method significantly improves 3D reconstruction quality and robustness at equal runtimes, while also supporting more general rendering methods such as shadow rays for direct illumination.<\/jats:p>","DOI":"10.1145\/3687914","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T15:46:04Z","timestamp":1732031164000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["3D Reconstruction with Fast Dipole Sums"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3858-0485","authenticated-orcid":false,"given":"Hanyu","family":"Chen","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0881-0351","authenticated-orcid":false,"given":"Bailey","family":"Miller","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6932-4642","authenticated-orcid":false,"given":"Ioannis","family":"Gkioulekas","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,19]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"George Vogiatzis, Engin Tola, and Anders Bjorholm Dahl.","author":"Aan\u00e6s Henrik","year":"2016","unstructured":"Henrik Aan\u00e6s, Rasmus Ramsb\u00f8l Jensen, George Vogiatzis, Engin Tola, and Anders Bjorholm Dahl. 2016. 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