{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T22:31:45Z","timestamp":1791930705528,"version":"4.3.4"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["611370"],"award-info":[{"award-number":["611370"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006785","name":"Google","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006785","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":[[2022,7]]},"abstract":"<jats:p>\n                    We introduce\n                    <jats:italic>neural dual contouring<\/jats:italic>\n                    (NDC), a new data-driven approach to mesh reconstruction based on dual contouring (DC). Like traditional DC, it produces exactly one vertex per grid cell and one quad for each grid edge intersection, a natural and efficient structure for reproducing sharp features. However, rather than computing vertex locations and edge crossings with hand-crafted functions that depend directly on difficult-to-obtain surface gradients, NDC uses a neural network to predict them. As a result, NDC can be trained to produce meshes from signed or unsigned distance fields, binary voxel grids, or point clouds (with or without normals); and it can produce open surfaces in cases where the input represents a sheet or partial surface. During experiments with five prominent datasets, we find that NDC, when trained on one of the datasets, generalizes well to the others. Furthermore, NDC provides better surface reconstruction accuracy, feature preservation, output complexity, triangle quality, and inference time in comparison to previous learned (e.g., neural marching cubes, convolutional occupancy networks) and traditional (e.g., Poisson) methods. Code and data are available at https:\/\/github.com\/czq142857\/NDC.\n                  <\/jats:p>","DOI":"10.1145\/3528223.3530108","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T17:06:27Z","timestamp":1658509587000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":99,"title":["Neural dual contouring"],"prefix":"10.1145","volume":"41","author":[{"given":"Zhiqin","family":"Chen","sequence":"first","affiliation":[{"name":"Simon Fraser University, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Tagliasacchi","sequence":"additional","affiliation":[{"name":"University of Toronto, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Funkhouser","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Nina Amenta Marshall Bern and Manolis Kamvysselis. 1998. A New Voronoi-Based Surface Reconstruction Algorithm. In SIGGRAPH. 415--421.","DOI":"10.1145\/280814.280947"},{"key":"e_1_2_2_2_1","volume-title":"SAL: Sign Agnostic Learning of Shapes From Raw Data. In CVPR. 2562--2571.","author":"Atzmon Matan","year":"2020","unstructured":"Matan Atzmon and Yaron Lipman. 2020. SAL: Sign Agnostic Learning of Shapes From Raw Data. In CVPR. 2562--2571."},{"key":"e_1_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Abhishek Badki Orazio Gallo Jan Kautz and Pradeep Sen. 2020. Meshlet priors for 3D mesh reconstruction. In CVPR. 2849--2858.","DOI":"10.1109\/CVPR42600.2020.00292"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12802"},{"key":"e_1_2_2_5_1","first-page":"349","article-title":"The ball-pivoting algorithm for surface reconstruction","volume":"5","author":"Bernardini Fausto","year":"1999","unstructured":"Fausto Bernardini, Joshua Mittleman, Holly Rushmeier, Cl\u00e1udio Silva, and Gabriel Taubin. 1999. The ball-pivoting algorithm for surface reconstruction. IEEE TVCG 5, 4 (1999), 349--359.","journal-title":"IEEE TVCG"},{"key":"e_1_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Bharat Lal Bhatnagar Garvita Tiwari Christian Theobalt and Gerard Pons-Moll. 2019. Multi-Garment Net: Learning to Dress 3D People from Images. In ICCV. 5420--5430.","DOI":"10.1109\/ICCV.2019.00552"},{"key":"e_1_2_2_7_1","volume-title":"Black","author":"Bogo Federica","year":"2014","unstructured":"Federica Bogo, Javier Romero, Matthew Loper, and Michael J. Black. 2014. FAUST: Dataset and evaluation for 3D mesh registration. In CVPR. 3794--3801."},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2012.03181.x"},{"key":"e_1_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Angel Chang Angela Dai Thomas Funkhouser Maciej Halber Matthias Niebner Manolis Savva Shuran Song Andy Zeng and Yinda Zhang. 2017. Matterport3D: Learning from RGB-D Data in Indoor Environments. In 3DV. 667--676.","DOI":"10.1109\/3DV.2017.00081"},{"key":"e_1_2_2_10_1","volume-title":"ShapeNet: An Information-Rich 3D Model Repository. arXiv preprint arXiv:1512.03012","author":"Chang Angel X.","year":"2015","unstructured":"Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. 2015. ShapeNet: An Information-Rich 3D Model Repository. arXiv preprint arXiv:1512.03012 (2015)."},{"key":"e_1_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Zhiqin Chen and Hao Zhang. 2019. Learning Implicit Fields for Generative Shape Modeling. In CVPR. 5932--5941.","DOI":"10.1109\/CVPR.2019.00609"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3478513.3480518"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-003-0217-z"},{"key":"e_1_2_2_15_1","doi-asserted-by":"crossref","unstructured":"Brian Curless and Marc Levoy. 1996. A volumetric method for building complex models from range images. In SIGGRAPH. 303--312.","DOI":"10.1145\/237170.237269"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2732197"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015706.1015775"},{"key":"e_1_2_2_18_1","first-page":"9936","article-title":"Learning deformable tetrahedral meshes for 3d reconstruction","volume":"33","author":"Gao Jun","year":"2020","unstructured":"Jun Gao, Wenzheng Chen, Tommy Xiang, Alec Jacobson, Morgan McGuire, and Sanja Fidler. 2020. Learning deformable tetrahedral meshes for 3d reconstruction. In NeurIPS, Vol. 33. 9936--9947.","journal-title":"NeurIPS"},{"key":"e_1_2_2_19_1","volume-title":"Heckbert","author":"Garland Michael","year":"1997","unstructured":"Michael Garland and Paul S. Heckbert. 1997. Surface simplification using quadric error metrics. In SIGGRAPH. 209--216."},{"key":"e_1_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Thibault Groueix Matthew Fisher Vladimir G. Kim Bryan C. Russell and Mathieu Aubry. 2018. A papier-m\u00e2ch\u00e9 approach to learning 3D surface generation. In CVPR. 216--224.","DOI":"10.1109\/CVPR.2018.00030"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392415"},{"key":"e_1_2_2_22_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778."},{"key":"e_1_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Chiyu Jiang Avneesh Sud Ameesh Makadia Jingwei Huang Matthias Nie\u00dfner and Thomas Funkhouser. 2020. Local implicit grid representations for 3d scenes. In CVPR. 6001--6010.","DOI":"10.1109\/CVPR42600.2020.00604"},{"key":"e_1_2_2_24_1","volume-title":"Dual Contouring of Hermite Data. ACM Transactions on graphics 21, 3","author":"Ju Tao","year":"2002","unstructured":"Tao Ju, Frank Losasso, Scott Schaefer, and Joe Warren. 2002. Dual Contouring of Hermite Data. ACM Transactions on graphics 21, 3 (2002), 339--346."},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487228.2487237"},{"key":"e_1_2_2_26_1","unstructured":"Sebastian Koch Albert Matveev Zhongshi Jiang Francis Williams Alexey Artemov Evgeny Burnaev Marc Alexa Denis Zorin and Daniele Panozzo. 2019. ABC: a big CAD model dataset for geometric deep learning. In CVPR. 9601--9611."},{"key":"e_1_2_2_27_1","unstructured":"Jaakko Lehtinen Jacob Munkberg Jon Hasselgren Samuli Laine Tero Karras Miika Aittala and Timo Aila. 2018. Noise2Noise: Learning Image Restoration without Clean Data. In ICML. 2965--2974."},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1080\/10867651.2003.10487582"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/1839778.1839808"},{"key":"e_1_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Yiyi Liao Simon Donne and Andreas Geiger. 2018. Deep marching cubes: Learning explicit surface representations. In CVPR. 2916--2925.","DOI":"10.1109\/CVPR.2018.00308"},{"key":"e_1_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Minghua Liu Xiaoshuai Zhang and Hao Su. 2020. Meshing point clouds with predicted intrinsic-extrinsic ratio guidance. In ECCV. 68--84.","DOI":"10.1007\/978-3-030-58598-3_5"},{"key":"e_1_2_2_32_1","volume-title":"Cline","author":"Lorensen William E.","year":"1987","unstructured":"William E. Lorensen and Harvey E. Cline. 1987. Marching Cubes: A High Resolution 3D Surface Construction Algorithm. In SIGGRAPH. 163--169."},{"key":"e_1_2_2_33_1","volume-title":"Occupancy Networks: Learning 3D Reconstruction in Function Space. In CVPR. 4455--4465.","author":"Mescheder Lars","year":"2019","unstructured":"Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. 2019. Occupancy Networks: Learning 3D Reconstruction in Function Space. In CVPR. 4455--4465."},{"key":"e_1_2_2_34_1","unstructured":"Zhenxing Mi Yiming Luo and Wenbing Tao. 2020. SSRNet: Scalable 3D Surface Reconstruction Network. In CVPR. 970--979."},{"key":"e_1_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Ben Mildenhall Pratul P. Srinivasan Matthew Tancik Jonathan T. Barron Ravi Ramamoorthi and Ren Ng. 2020. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. In ECCV. 405--421.","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"e_1_2_2_36_1","volume-title":"Dual Marching Cubes","author":"Nielson Gregory M.","unstructured":"Gregory M. Nielson. 2004. Dual Marching Cubes. In IEEE Visualization. 489--496."},{"key":"e_1_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Jeong Joon Park Peter Florence Julian Straub Richard Newcombe and Steven Love-grove. 2019. DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. In CVPR. 165--174.","DOI":"10.1109\/CVPR.2019.00025"},{"key":"e_1_2_2_38_1","volume-title":"Symp. on Geometry Processing. 23--32","author":"Pauly Mark","unstructured":"Mark Pauly, Niloy J. Mitra, Joachim Giesen, Markus H. Gross, and Leonidas J. Guibas. 2005. Example-based 3D scan completion. In Symp. on Geometry Processing. 23--32."},{"key":"e_1_2_2_39_1","volume-title":"NeurIPS","volume":"34","author":"Peng Songyou","year":"2021","unstructured":"Songyou Peng, Chiyu Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, and Andreas Geiger. 2021. Shape as points: A differentiable Poisson solver. In NeurIPS, Vol. 34."},{"key":"e_1_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Songyou Peng Michael Niemeyer Lars Mescheder Marc Pollefeys and Andreas Geiger. 2020. Convolutional occupancy networks. In ECCV. 523--540.","DOI":"10.1007\/978-3-030-58580-8_31"},{"key":"e_1_2_2_41_1","first-page":"5105","article-title":"PointNet++: Deep hierarchical feature learning on point sets in a metric space","volume":"30","author":"Qi Charles R.","year":"2017","unstructured":"Charles R. Qi, Li Yi, Hao Su, and Leonidas J. Guibas. 2017. PointNet++: Deep hierarchical feature learning on point sets in a metric space. In NeurIPS, Vol. 30. 5105--5114.","journal-title":"NeurIPS"},{"key":"e_1_2_2_42_1","doi-asserted-by":"crossref","unstructured":"Marie-Julie Rakotosaona Paul Guerrero Noam Aigerman Niloy Mitra and Maks Ovsjanikov. 2021. Learning Delaunay Surface Elements for Mesh Reconstruction. In CVPR. 22--31.","DOI":"10.1109\/CVPR46437.2021.00009"},{"key":"e_1_2_2_43_1","first-page":"22468","article-title":"MeshSDF: differentiable iso-surface extraction","volume":"33","author":"Remelli Edoardo","year":"2020","unstructured":"Edoardo Remelli, Artem Lukoianov, Stephan R. Richter, Beno\u00eet Guillard, Timur Bagautdinov, Pierre Baque, and Pascal Fua. 2020. MeshSDF: differentiable iso-surface extraction. In NeurIPS, Vol. 33. 22468--22478.","journal-title":"NeurIPS"},{"key":"e_1_2_2_44_1","first-page":"610","article-title":"Manifold dual contouring","volume":"13","author":"Schaefer Scott","year":"2007","unstructured":"Scott Schaefer, Tao Ju, and Joe Warren. 2007. Manifold dual contouring. IEEE TVCG 13, 3 (2007), 610--619.","journal-title":"IEEE TVCG"},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2009.01389.x"},{"key":"e_1_2_2_46_1","doi-asserted-by":"crossref","unstructured":"Nicholas Sharp and Maks Ovsjanikov. 2020. PointTriNet: Learned Triangulation of 3D Point Sets. In ECCV. 762--778.","DOI":"10.1007\/978-3-030-58592-1_45"},{"key":"e_1_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366199"},{"key":"e_1_2_2_48_1","first-page":"7462","article-title":"Implicit neural representations with periodic activation functions","volume":"33","author":"Sitzmann Vincent","year":"2020","unstructured":"Vincent Sitzmann, Julien NP Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein. 2020. Implicit neural representations with periodic activation functions. In NeurIPS, Vol. 33. 7462--7473.","journal-title":"NeurIPS"},{"key":"e_1_2_2_49_1","doi-asserted-by":"crossref","unstructured":"Jiapeng Tang Jiabao Lei Dan Xu Feiying Ma Kui Jia and Lei Zhang. 2021. SAConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks. In ICCV. 6484--6493.","DOI":"10.1109\/ICCV48922.2021.00644"},{"key":"e_1_2_2_50_1","doi-asserted-by":"crossref","unstructured":"Francis Williams Teseo Schneider Claudio Silva Denis Zorin Joan Bruna and Daniele Panozzo. 2019. Deep geometric prior for surface reconstruction. In CVPR. 10130--10139.","DOI":"10.1109\/CVPR.2019.01037"},{"key":"e_1_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01900346"},{"key":"e_1_2_2_52_1","volume-title":"Thingi10K: a dataset of 10,000 3D-printing models. arXiv preprint arXiv:1605.04797","author":"Zhou Qingnan","year":"2016","unstructured":"Qingnan Zhou and Alec Jacobson. 2016. Thingi10K: a dataset of 10,000 3D-printing models. arXiv preprint arXiv:1605.04797 (2016)."}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3528223.3530108","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3528223.3530108","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T15:02:26Z","timestamp":1750172546000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3528223.3530108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":51,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["10.1145\/3528223.3530108"],"URL":"https:\/\/doi.org\/10.1145\/3528223.3530108","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7]]},"assertion":[{"value":"2022-07-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}