{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T08:29:22Z","timestamp":1742977762877,"version":"3.40.3"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031734632"},{"type":"electronic","value":"9783031734649"}],"license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"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\/978-3-031-73464-9_14","type":"book-chapter","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T09:37:00Z","timestamp":1733218620000},"page":"223-239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CPT-VR: Improving Surface Rendering via\u00a0Closest Point Transform with\u00a0View-Reflection Appearance"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4367-0816","authenticated-orcid":false,"given":"Zhipeng","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5809-4829","authenticated-orcid":false,"given":"Yongqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3159-0034","authenticated-orcid":false,"given":"Chen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6047-0472","authenticated-orcid":false,"given":"Lincheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6546-4525","authenticated-orcid":false,"given":"Sida","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1926-5597","authenticated-orcid":false,"given":"Xiaowei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5420-0516","authenticated-orcid":false,"given":"Changjie","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0269-5649","authenticated-orcid":false,"given":"Xin","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,4]]},"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"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Darmon, F., Bascle, B., Devaux, J.C., Monasse, P., Aubry, M.: Improving neural implicit surfaces geometry with patch warping. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6260\u20136269 (2022)","DOI":"10.1109\/CVPR52688.2022.00616"},{"key":"14_CR3","unstructured":"Fu, Q., Xu, Q., Ong, Y.S., Tao, W.: Geo-neus: geometry-consistent neural implicit surfaces learning for multi-view reconstruction. arXiv preprint arXiv:2205.15848 (2022)"},{"key":"14_CR4","unstructured":"Gropp, A., Yariv, L., Haim, N., Atzmon, M., Lipman, Y.: Implicit geometric regularization for learning shapes. In: International Conference on Machine Learning, pp. 3789\u20133799. PMLR (2020)"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Gu, X., Fan, Z., Zhu, S., Dai, Z., Tan, F., Tan, P.: Cascade cost volume for high-resolution multi-view stereo and stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2495\u20132504 (2020)","DOI":"10.1109\/CVPR42600.2020.00257"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Guo, Y.C., et al.: VMesh: hybrid volume-mesh representation for efficient view synthesis. arXiv preprint arXiv:2303.16184 (2023)","DOI":"10.1145\/3610548.3618161"},{"issue":"10","key":"14_CR7","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/s003710050084","volume":"12","author":"JC Hart","year":"1996","unstructured":"Hart, J.C.: Sphere tracing: a geometric method for the antialiased ray tracing of implicit surfaces. Vis. Comput. 12(10), 527\u2013545 (1996)","journal-title":"Vis. Comput."},{"key":"14_CR8","first-page":"22856","volume":"35","author":"J Hasselgren","year":"2022","unstructured":"Hasselgren, J., Hofmann, N., Munkberg, J.: Shape, light, and material decomposition from images using Monte Carlo rendering and denoising. Adv. Neural. Inf. Process. Syst. 35, 22856\u201322869 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"14_CR9","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"},{"issue":"6","key":"14_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3414685.3417861","volume":"39","author":"S Laine","year":"2020","unstructured":"Laine, S., Hellsten, J., Karras, T., Seol, Y., Lehtinen, J., Aila, T.: Modular primitives for high-performance differentiable rendering. ACM Trans. Graph. (TOG) 39(6), 1\u201314 (2020)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"14_CR11","unstructured":"Li, H., Yang, X., Zhai, H., Liu, Y., Bao, H., Zhang, G.: Vox-Surf: voxel-based implicit surface representation. IEEE Trans. Vis. Comput. Graph. (2022)"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Neuralangelo: High-fidelity neural surface reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8456\u20138465 (2023)","DOI":"10.1109\/CVPR52729.2023.00817"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Liu, H.T.D., Williams, F., Jacobson, A., Fidler, S., Litany, O.: Learning smooth neural functions via Lipschitz regularization. In: ACM SIGGRAPH 2022 Conference Proceedings, pp. 1\u201313 (2022)","DOI":"10.1145\/3528233.3530713"},{"issue":"4","key":"14_CR14","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1145\/37402.37422","volume":"21","author":"WE Lorensen","year":"1987","unstructured":"Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. ACM Siggraph Comput. Graph. 21(4), 163\u2013169 (1987)","journal-title":"ACM Siggraph Comput. Graph."},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Ma, B., Zhou, J., Liu, Y.S., Han, Z.: Towards better gradient consistency for neural signed distance functions via level set alignment. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17724\u201317734 (2023)","DOI":"10.1109\/CVPR52729.2023.01700"},{"key":"14_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/978-3-031-20086-1_41","volume-title":"Computer Vision \u2013 ECCV 2022","author":"I Mehta","year":"2022","unstructured":"Mehta, I., Chandraker, M., Ramamoorthi, R.: A level set theory for neural implicit evolution under explicit flows. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13662, pp. 711\u2013729. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20086-1_41"},{"key":"14_CR17","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_CR18","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_CR19","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."},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Sun, J., Chen, X., Wang, Q., Li, Z., Averbuch-Elor, H., Zhou, X., Snavely, N.: Neural 3D reconstruction in the wild. In: ACM SIGGRAPH 2022 Conference Proceedings, pp.\u00a01\u20139 (2022)","DOI":"10.1145\/3528233.3530718"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Verbin, D., Hedman, P., Mildenhall, B., Zickler, T., Barron, J.T., Srinivasan, P.P.: Ref-NeRF: structured view-dependent appearance for neural radiance fields. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5481\u20135490. IEEE (2022)","DOI":"10.1109\/CVPR52688.2022.00541"},{"issue":"4","key":"14_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3528223.3530139","volume":"41","author":"D Vicini","year":"2022","unstructured":"Vicini, D., Speierer, S., Jakob, W.: Differentiable signed distance function rendering. ACM Trans. Graph. (TOG) 41(4), 1\u201318 (2022)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"14_CR23","unstructured":"Walker, T., Mariotti, O., Vaxman, A., Bilen, H.: Explicit neural surfaces: learning continuous geometry with deformation fields. arXiv preprint arXiv:2306.02956 (2023)"},{"key":"14_CR24","first-page":"27171","volume":"34","author":"P Wang","year":"2021","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. Adv. Neural. Inf. Process. Syst. 34, 27171\u201327183 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Y., Han, Q., Habermann, M., Daniilidis, K., Theobalt, C., Liu, L.: NeuS2: fast learning of neural implicit surfaces for multi-view reconstruction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3295\u20133306 (2023)","DOI":"10.1109\/ICCV51070.2023.00305"},{"key":"14_CR26","first-page":"1966","volume":"35","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Skorokhodov, I., Wonka, P.: HF-NeuS: improved surface reconstruction using high-frequency details. Adv. Neural. Inf. Process. Syst. 35, 1966\u20131978 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"14_CR27","unstructured":"Wang, Y., Skorokhodov, I., Wonka, P.: Improved surface reconstruction using high-frequency details. arXiv preprint arXiv:2206.07850 (2022)"},{"key":"14_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Y., Skorokhodov, I., Wonka, P.: PET-NeuS: positional encoding tri-planes for neural surfaces. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12598\u201312607 (2023)","DOI":"10.1109\/CVPR52729.2023.01212"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Worchel, M., Diaz, R., Hu, W., Schreer, O., Feldmann, I., Eisert, P.: Multi-view mesh reconstruction with neural deferred shading. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6187\u20136197 (2022)","DOI":"10.1109\/CVPR52688.2022.00609"},{"key":"14_CR30","unstructured":"Wu, T., et al.: Voxurf: voxel-based efficient and accurate neural surface reconstruction. arXiv preprint arXiv:2208.12697 (2022)"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Yao, Y., et al.: BlendedMVS: a large-scale dataset for generalized multi-view stereo networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1790\u20131799 (2020)","DOI":"10.1109\/CVPR42600.2020.00186"},{"key":"14_CR32","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_CR33","doi-asserted-by":"crossref","unstructured":"Yariv, L., et al.: BakedSDF: meshing neural SDFs for real-time view synthesis. arXiv preprint arXiv:2302.14859 (2023)","DOI":"10.1145\/3588432.3591536"},{"key":"14_CR34","first-page":"2492","volume":"33","author":"L Yariv","year":"2020","unstructured":"Yariv, L., et al.: Multiview neural surface reconstruction by disentangling geometry and appearance. Adv. Neural. Inf. Process. Syst. 33, 2492\u20132502 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"14_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Critical regularizations for neural surface reconstruction in the wild. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6270\u20136279 (2022)","DOI":"10.1109\/CVPR52688.2022.00617"},{"key":"14_CR36","unstructured":"Zhang, J., Yao, Y., Li, S., Luo, Z., Fang, T.: Visibility-aware multi-view stereo network. In: British Machine Vision Conference (BMVC) (2020)"},{"key":"14_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, J., Yao, Y., Quan, L.: Learning signed distance field for multi-view surface reconstruction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6525\u20136534 (2021)","DOI":"10.1109\/ICCV48922.2021.00646"},{"key":"14_CR38","unstructured":"Zhang, K., Riegler, G., Snavely, N., Koltun, V.: NeRF++: analyzing and improving neural radiance fields. arXiv preprint arXiv:2010.07492 (2020)"},{"key":"14_CR39","unstructured":"Zhang, Y., Zhu, J., Lin, L.: FastMesh: fast surface reconstruction by hexagonal mesh-based neural rendering. arXiv preprint arXiv:2305.17858 (2023)"},{"key":"14_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: Towards unbiased volume rendering of neural implicit surfaces with geometry priors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4359\u20134368 (2023)","DOI":"10.1109\/CVPR52729.2023.00424"},{"key":"14_CR41","doi-asserted-by":"crossref","unstructured":"Zhuang, Y., et al.: Anti-aliased neural implicit surfaces with encoding level of detail. In: SIGGRAPH Asia 2023 Conference Papers, pp. 1\u201310 (2023)","DOI":"10.1145\/3610548.3618197"}],"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\/978-3-031-73464-9_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T10:08:06Z","timestamp":1733220486000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73464-9_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"ISBN":["9783031734632","9783031734649"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73464-9_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"4 December 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"}}]}}