{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T05:57:54Z","timestamp":1761631074474,"version":"3.40.3"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031726729"},{"type":"electronic","value":"9783031726736"}],"license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-72673-6_14","type":"book-chapter","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T16:03:50Z","timestamp":1729526630000},"page":"251-269","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Differentiable Convex Polyhedra Optimization from\u00a0Multi-view Images"],"prefix":"10.1007","author":[{"given":"Daxuan","family":"Ren","sequence":"first","affiliation":[]},{"given":"Haiyi","family":"Mei","sequence":"additional","affiliation":[]},{"given":"Hezi","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Jianmin","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Jianfei","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Bangaru, S.P., et al.: Differentiable rendering of neural SDFs through reparameterization. In: SIGGRAPH Asia 2022 Conference Papers, pp.\u00a01\u20139 (2022)","DOI":"10.1145\/3550469.3555397"},{"issue":"6","key":"14_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3414685.3417833","volume":"39","author":"SP Bangaru","year":"2020","unstructured":"Bangaru, S.P., Li, T.M., Durand, F.: Unbiased warped-area sampling for differentiable rendering. ACM Trans. Graph. (TOG) 39(6), 1\u201318 (2020)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"14_CR3","unstructured":"Chang, A.X., et\u00a0al.: ShapeNet: An information-rich 3D model repository (2015). arXiv preprint arXiv:1512.03012"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Z., Tagliasacchi, A., Zhang, H.: BSP-Net: generating compact meshes via binary space partitioning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 45\u201354 (2020)","DOI":"10.1109\/CVPR42600.2020.00012"},{"key":"14_CR5","unstructured":"Community, B.O.: Blender - a 3D modelling and rendering package. Blender Foundation, Stichting Blender Foundation, Amsterdam (2018). http:\/\/www.blender.org"},{"key":"14_CR6","doi-asserted-by":"publisher","unstructured":"De\u00a0Berg, M.: Computational geometry: algorithms and applications. Springer Science & Business Media (2000). https:\/\/doi.org\/10.1007\/978-3-540-77974-2","DOI":"10.1007\/978-3-540-77974-2"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Deng, B., Genova, K., Yazdani, S., Bouaziz, S., Hinton, G., Tagliasacchi, A.: CvxNet: learnable convex decomposition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 31\u201344 (2020)","DOI":"10.1109\/CVPR42600.2020.00011"},{"issue":"1","key":"14_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2835487","volume":"35","author":"K Guo","year":"2015","unstructured":"Guo, K., Zou, D., Chen, X.: 3D mesh labeling via deep convolutional neural networks. ACM Trans. Graph. (TOG) 35(1), 1\u201312 (2015)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Hao, Z., Averbuch-Elor, H., Snavely, N., Belongie, S.: DualSDF: semantic shape manipulation using a two-level representation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7631\u20137641 (2020)","DOI":"10.1109\/CVPR42600.2020.00765"},{"key":"14_CR10","unstructured":"Jakob, W., et al.: Mitsuba 3 renderer (2022). https:\/\/mitsuba-renderer.org"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Jensen, R., Dahl, A., Vogiatzis, G., Tola, E., Aan\u00e6s, H.: Large scale multi-view stereopsis evaluation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 406\u2013413 (2014)","DOI":"10.1109\/CVPR.2014.59"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Ji, D., Han, Z., Zwicker, M.: SDFDiff: differentiable rendering of signed distance fields for 3D shape optimization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern recognition, pp. 1251\u20131261 (2020)","DOI":"10.1109\/CVPR42600.2020.00133"},{"key":"14_CR13","unstructured":"Kania, K., Zieba, M., Kajdanowicz, T.: UCSG-Net\u2013unsupervised discovering of constructive solid geometry tree (2020). arXiv preprint arXiv:2006.09102"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Kerbl, B., Kopanas, G., Leimk\u00fchler, T., Drettakis, G.: 3D gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. 42(4), 139\u20131 (2023)","DOI":"10.1145\/3592433"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Laine, S., Hellsten, J., Karras, T., Seol, Y., Lehtinen, J., Aila, T.: Modular primitives for high-performance differentiable rendering. ACM Trans. Graph. 39(6), 1\u201314 (2020)","DOI":"10.1145\/3414685.3417861"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Li, P., Guo, J., Zhang, X., Yan, D.M.: SECAD-Net: self-supervised CAD reconstruction by learning sketch-extrude operations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16816\u201316826 (2023)","DOI":"10.1109\/CVPR52729.2023.01613"},{"issue":"6","key":"14_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3272127.3275055","volume":"37","author":"TM Li","year":"2018","unstructured":"Li, T.M., Aittala, M., Durand, F., Lehtinen, J.: Differentiable monte Carlo ray tracing through edge sampling. ACM Trans. Graph. (TOG) 37(6), 1\u201311 (2018)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Liao, Y., Donne, S., Geiger, A.: Deep marching cubes: learning explicit surface representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2916\u20132925 (2018)","DOI":"10.1109\/CVPR.2018.00308"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Lien, J.M., Amato, N.M.: Approximate convex decomposition. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 457\u2013458 (2004)","DOI":"10.1145\/997817.997889"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Lien, J.M., Amato, N.M.: Approximate convex decomposition of Polyhedra. In: Proceedings of the 2007 ACM Symposium on Solid and Physical Modeling, pp. 121\u2013131 (2007)","DOI":"10.1145\/1236246.1236265"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Liu, S., Li, T., Chen, W., Li, H.: Soft rasterizer: a differentiable renderer for image-based 3D reasoning. In: The IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00780"},{"issue":"6","key":"14_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3355089.3356510","volume":"38","author":"G Loubet","year":"2019","unstructured":"Loubet, G., Holzschuch, N., Jakob, W.: Reparameterizing discontinuous integrands for differentiable rendering. ACM Trans. Graph. (TOG) 38(6), 1\u201314 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Maturana, D., Scherer, S.: VoxNet: A 3D convolutional neural network for real-time object recognition. In: 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 922\u2013928. IEEE (2015)","DOI":"10.1109\/IROS.2015.7353481"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3D reconstruction in function space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4460\u20134470 (2019)","DOI":"10.1109\/CVPR.2019.00459"},{"key":"14_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/978-3-030-58452-8_24","volume-title":"Computer Vision \u2013 ECCV 2020","author":"B Mildenhall","year":"2020","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405\u2013421. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_24"},{"key":"14_CR26","unstructured":"Monnier, T., Austin, J., Kanazawa, A., Efros, A.A., Aubry, M.: Differentiable blocks world: qualitative 3D decomposition by rendering primitives. In: NeurIPS (2023)"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Munkberg, J., et al.: Extracting triangular 3D models, materials, and lighting from images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8280\u20138290 (2022)","DOI":"10.1109\/CVPR52688.2022.00810"},{"key":"14_CR28","unstructured":"Nichol, A., Jun, H., Dhariwal, P., Mishkin, P., Chen, M.: Point-e: A system for generating 3D point clouds from complex prompts (2022). arXiv preprint arXiv:2212.08751"},{"key":"14_CR29","doi-asserted-by":"publisher","unstructured":"Nicolet, B., Jacobson, A., Jakob, W.: Large steps in inverse rendering of geometry. ACM Trans. Graph. (Proceedings of SIGGRAPH Asia) 40(6), 1\u201313 (2021). https:\/\/doi.org\/10.1145\/3478513.3480501, https:\/\/rgl.epfl.ch\/publications\/Nicolet2021Large","DOI":"10.1145\/3478513.3480501"},{"issue":"6","key":"14_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3355089.3356498","volume":"38","author":"M Nimier-David","year":"2019","unstructured":"Nimier-David, M., Vicini, D., Zeltner, T., Jakob, W.: Mitsuba 2: a retargetable forward and inverse renderer. ACM Trans. Graph. (TOG) 38(6), 1\u201317 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1520\u20131528 (2015)","DOI":"10.1109\/ICCV.2015.178"},{"key":"14_CR32","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 165\u2013174 (2019)","DOI":"10.1109\/CVPR.2019.00025"},{"key":"14_CR33","doi-asserted-by":"crossref","unstructured":"Paschalidou, D., Ulusoy, A.O., Geiger, A.: Superquadrics revisited: Learning 3D shape parsing beyond cuboids. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10344\u201310353 (2019)","DOI":"10.1109\/CVPR.2019.01059"},{"key":"14_CR34","unstructured":"Preparata, F.P., Shamos, M.I.: Computational geometry: an introduction. Springer Science & Business Media (2012)"},{"key":"14_CR35","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652\u2013660 (2017)"},{"key":"14_CR36","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Su, H., Nie\u00dfner, M., Dai, A., Yan, M., Guibas, L.J.: Volumetric and multi-view CNNs for object classification on 3D data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5648\u20135656 (2016)","DOI":"10.1109\/CVPR.2016.609"},{"key":"14_CR37","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"14_CR38","unstructured":"Ravi, N., et al.: Accelerating 3D deep learning with PyTorch3D (2020). arXiv:2007.08501"},{"key":"14_CR39","doi-asserted-by":"crossref","unstructured":"Ren, D., et\u00a0al.: CSG-stump: a learning friendly cSG-like representation for interpretable shape parsing (2021). arXiv preprint arXiv:2108.11305","DOI":"10.1109\/ICCV48922.2021.01225"},{"key":"14_CR40","doi-asserted-by":"publisher","unstructured":"Ren, D., Zheng, J., Cai, J., Li, J., Zhang, J.: ExtrudeNet: unsupervised inverse sketch-and-extrude for shape parsing. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022. ECCV 2022. LNCS, vol. 13662. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20086-1_28","DOI":"10.1007\/978-3-031-20086-1_28"},{"key":"14_CR41","first-page":"6087","volume":"34","author":"T Shen","year":"2021","unstructured":"Shen, T., Gao, J., Yin, K., Liu, M.Y., Fidler, S.: Deep marching tetrahedra: a hybrid representation for high-resolution 3D shape synthesis. Adv. Neural. Inf. Process. Syst. 34, 6087\u20136101 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"4","key":"14_CR42","first-page":"1","volume":"42","author":"T Shen","year":"2023","unstructured":"Shen, T., et al.: Flexible isosurface extraction for gradient-based mesh optimization. ACM Trans. Graph. (TOG) 42(4), 1\u201316 (2023)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"14_CR43","doi-asserted-by":"crossref","unstructured":"Tulsiani, S., Su, H., Guibas, L.J., Efros, A.A., Malik, J.: Learning shape abstractions by assembling volumetric primitives. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2635\u20132643 (2017)","DOI":"10.1109\/CVPR.2017.160"},{"key":"14_CR44","doi-asserted-by":"publisher","unstructured":"Vicini, D., Speierer, S., Jakob, W.: Differentiable signed distance function rendering. Trans. Graph. (Proceedings of SIGGRAPH) 41(4), 125:1\u2013125:18 (2022). https:\/\/doi.org\/10.1145\/3528223.3530139","DOI":"10.1145\/3528223.3530139"},{"key":"14_CR45","doi-asserted-by":"crossref","unstructured":"Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., Jiang, Y.G.: Pixel2mesh: generating 3D mesh models from single RGB images. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 52\u201367 (2018)","DOI":"10.1007\/978-3-030-01252-6_4"},{"key":"14_CR46","unstructured":"Wang, P., Liu, L., Liu, Y., Theobalt, C., Komura, T., Wang, W.: NeuS: Learning neural implicit surfaces by volume rendering for multi-view reconstruction (2021). arXiv preprint arXiv:2106.10689"},{"issue":"4","key":"14_CR47","first-page":"1","volume":"36","author":"PS Wang","year":"2017","unstructured":"Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: O-CNN: octree-based convolutional neural networks for 3D shape analysis. ACM Trans. Graph. (TOG) 36(4), 1\u201311 (2017)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"5","key":"14_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. (TOG) 38(5), 1\u201312 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"14_CR49","doi-asserted-by":"crossref","unstructured":"Wen, C., Zhang, Y., Li, Z., Fu, Y.: Pixel2mesh++: multi-view 3D mesh generation via deformation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1042\u20131051 (2019)","DOI":"10.1109\/ICCV.2019.00113"},{"key":"14_CR50","doi-asserted-by":"crossref","unstructured":"Wu, Q., et al.: Object-compositional neural implicit surfaces (2022). arXiv preprint arXiv:2207.09686","DOI":"10.1109\/ICCV51070.2023.01989"},{"key":"14_CR51","doi-asserted-by":"crossref","unstructured":"Xu, Q., et al.: Point-NeRF: point-based neural radiance fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5438\u20135448 (2022)","DOI":"10.1109\/CVPR52688.2022.00536"},{"key":"14_CR52","first-page":"4805","volume":"34","author":"L Yariv","year":"2021","unstructured":"Yariv, L., Gu, J., Kasten, Y., Lipman, Y.: Volume rendering of neural implicit surfaces. Adv. Neural. Inf. Process. Syst. 34, 4805\u20134815 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"14_CR53","unstructured":"Yu, F., Chen, Q., Tanveer, M., Amiri, A.M., Zhang, H.: DualCSG: Learning dual CSG trees for general and compact cad modeling (2023). arXiv preprint arXiv:2301.11497"},{"key":"14_CR54","doi-asserted-by":"crossref","unstructured":"Yu, F., et al.: CAPRI-Net: Learning compact cad shapes with adaptive primitive assembly (2021). arXiv preprint arXiv:2104.05652","DOI":"10.1109\/CVPR52688.2022.01147"},{"key":"14_CR55","unstructured":"Yu, Z., Peng, S., Niemeyer, M., Sattler, T., Geiger, A.: MonoSDF: exploring monocular geometric cues for neural implicit surface reconstruction. In: Advances in Neural Information Processing Systems (NeurIPS) (2022)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72673-6_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T16:08:16Z","timestamp":1729526896000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72673-6_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,22]]},"ISBN":["9783031726729","9783031726736"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72673-6_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,22]]},"assertion":[{"value":"22 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}