{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T18:19:51Z","timestamp":1774462791478,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Chongqing Municipal Construction Science & Technology Program","award":["Project No. Chengkezi [2023] 3-20"],"award-info":[{"award-number":["Project No. Chengkezi [2023] 3-20"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s00371-026-04412-2","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T11:19:04Z","timestamp":1773659944000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive diffusion refinement for enhanced 3D surface reconstruction under geometric complexity"],"prefix":"10.1007","volume":"42","author":[{"given":"Fei","family":"Chen","sequence":"first","affiliation":[]},{"given":"Ying","family":"He","sequence":"additional","affiliation":[]},{"given":"Xiantao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Gong","family":"He","sequence":"additional","affiliation":[]},{"given":"Bingcai","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"issue":"12","key":"4412_CR1","doi-asserted-by":"publisher","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","volume":"43","author":"Y Guo","year":"2021","unstructured":"Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., Bennamoun, M.: Deep learning for 3d point clouds: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(12), 4338\u20134364 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"4412_CR2","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)"},{"issue":"12","key":"4412_CR3","doi-asserted-by":"publisher","first-page":"9005","DOI":"10.1007\/s00371-024-03291-9","volume":"40","author":"D Li","year":"2024","unstructured":"Li, D., Li, X., Li, C.: Embrace descriptors that use point pairs feature. Vis. Comput. 40(12), 9005\u20139016 (2024)","journal-title":"Vis. Comput."},{"issue":"12","key":"4412_CR4","doi-asserted-by":"publisher","first-page":"8881","DOI":"10.1007\/s00371-024-03281-x","volume":"40","author":"Y Chen","year":"2024","unstructured":"Chen, Y., Chen, B., Xian, W., Wang, J., Huang, Y., Chen, M.: LGFDR: local and global feature denoising reconstruction for unsupervised anomaly detection. Vis. Comput. 40(12), 8881\u20138894 (2024)","journal-title":"Vis. Comput."},{"key":"4412_CR5","doi-asserted-by":"crossref","unstructured":"Choy, C.B., Xu, D., Gwak, J., Chen, K., Savarese, S.: 3D\u2013R2N2: a unified approach for single and multi-view 3d object reconstruction. In: Computer Vision\u2014ECCV 2016, pp. 628\u2013644 (2016)","DOI":"10.1007\/978-3-319-46484-8_38"},{"key":"4412_CR6","doi-asserted-by":"crossref","unstructured":"Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: A Papier\u2013Mache approach to learning 3d surface generation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 216\u2013224 (2018)","DOI":"10.1109\/CVPR.2018.00030"},{"key":"4412_CR7","doi-asserted-by":"crossref","unstructured":"Yuan, W., Khot, T., Held, D., Mertz, C., Hebert, M.: PCN: point completion network. In: 2018 International Conference on 3D Vision (3DV), pp. 728\u2013737 (2018)","DOI":"10.1109\/3DV.2018.00088"},{"key":"4412_CR8","doi-asserted-by":"crossref","unstructured":"Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3d reconstruction in function space. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4455\u20134465 (2019)","DOI":"10.1109\/CVPR.2019.00459"},{"key":"4412_CR9","doi-asserted-by":"crossref","unstructured":"Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: a deep representation for volumetric shapes. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1912\u20131920 (2015)","DOI":"10.1109\/CVPR.2015.7298801"},{"issue":"4","key":"4412_CR10","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1145\/3386569.3392415","volume":"39","author":"R Hanocka","year":"2020","unstructured":"Hanocka, R., Metzer, G., Giryes, R., Cohen-Or, D.: Point2mesh: a self-prior for deformable meshes. ACM Trans. Graph. (TOG) 39(4), 126\u2013131 (2020)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"4","key":"4412_CR11","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1109\/2945.817351","volume":"5","author":"F Bernardini","year":"1999","unstructured":"Bernardini, F., Mittleman, J., Rushmeier, H., Silva, C., Taubin, G.: The ball-pivoting algorithm for surface reconstruction. IEEE Trans. Visual Comput. Graph. 5(4), 349\u2013359 (1999)","journal-title":"IEEE Trans. Visual Comput. Graph."},{"key":"4412_CR12","doi-asserted-by":"crossref","unstructured":"Peng, S., Niemeyer, M., Mescheder, L., Pollefeys, M., Geiger, A.: Convolutional occupancy networks. In: Computer Vision\u2014ECCV, pp. 523\u2013540 (2020)","DOI":"10.1007\/978-3-030-58580-8_31"},{"key":"4412_CR13","doi-asserted-by":"crossref","unstructured":"Boulch, A., Marlet, R.: Poco: Point convolution for surface reconstruction. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6292\u20136304 (2022)","DOI":"10.1109\/CVPR52688.2022.00620"},{"issue":"4","key":"4412_CR14","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.vrih.2023.06.005","volume":"6","author":"L Yujie","year":"2024","unstructured":"Yujie, L., Xiaorui, S., Wenbin, S., Yafu, Y.: S2A-Net: combining local spectral and spatial point grouping for point cloud processing. Virtual Real. Intell. Hardware 6(4), 267\u2013279 (2024)","journal-title":"Virtual Real. Intell. Hardware"},{"key":"4412_CR15","doi-asserted-by":"crossref","unstructured":"Dutt, N.S., Muralikrishnan, S., Mitra, N.J.: Diffusion 3d features (diff3f): decorating untextured shapes with distilled semantic features. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4494\u20134504 (2024)","DOI":"10.1109\/CVPR52733.2024.00430"},{"key":"4412_CR16","doi-asserted-by":"crossref","unstructured":"Xu, C., Ling, H., Fidler, S., Litany, O.: 3difftection: 3D object detection with geometry-aware diffusion features. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10617\u201310627 (2024)","DOI":"10.1109\/CVPR52733.2024.01010"},{"key":"4412_CR17","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: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 165\u2013174 (2019)","DOI":"10.1109\/CVPR.2019.00025"},{"key":"4412_CR18","doi-asserted-by":"crossref","unstructured":"Huang, J., Gojcic, Z., Atzmon, M., Litany, O., Fidler, S., Williams, F.: Neural kernel surface reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4369\u20134379 (2023)","DOI":"10.1109\/CVPR52729.2023.00425"},{"issue":"1","key":"4412_CR19","doi-asserted-by":"publisher","first-page":"2201","DOI":"10.1002\/cav.2201","volume":"35","author":"X Zhu","year":"2024","unstructured":"Zhu, X., Yao, X., Zhang, J., Zhu, M., You, L., Yang, X., Zhang, J., Zhao, H., Zeng, D.: TMSDNet: transformer with multi-scale dense network for single and multi-view 3d reconstruction. Comput. Anim. Virtual Worlds 35(1), 2201 (2024)","journal-title":"Comput. Anim. Virtual Worlds"},{"issue":"3","key":"4412_CR20","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1002\/cav.2277","volume":"35","author":"J Feng","year":"2024","unstructured":"Feng, J., He, C., Wang, G., Wang, M.: S-LASSIE: structure and smoothness enhanced learning from sparse image ensemble for 3d articulated shape reconstruction. Comput. Anim. Virtual Worlds 35(3), 2277 (2024)","journal-title":"Comput. Anim. Virtual Worlds"},{"key":"4412_CR21","doi-asserted-by":"publisher","first-page":"6198","DOI":"10.1109\/TMM.2025.3565935","volume":"27","author":"Y Wen","year":"2025","unstructured":"Wen, Y., Luo, B., Shi, W., Ji, J., Cao, W., Yang, X., Sheng, B.: Sat-net: structure-aware transformer-based attention fusion network for low-quality retinal fundus images enhancement. IEEE Trans. Multimedia 27, 6198\u20136210 (2025)","journal-title":"IEEE Trans. Multimedia"},{"key":"4412_CR22","doi-asserted-by":"crossref","unstructured":"Long, X., Lin, C., Liu, L., Liu, Y., Wang, P., Theobalt, C., Komura, T., Wang, W.: Neuraludf: learning unsigned distance fields for multi-view reconstruction of surfaces with arbitrary topologies. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20834\u201320843 (2023)","DOI":"10.1109\/CVPR52729.2023.01996"},{"key":"4412_CR23","first-page":"21638","volume":"33","author":"J Chibane","year":"2020","unstructured":"Chibane, J., Pons-Moll, G., et al.: Neural unsigned distance fields for implicit function learning. Adv. Neural. Inf. Process. Syst. 33, 21638\u201321652 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"4412_CR24","unstructured":"Ma, B., Han, Z., Liu, Y.-S., Zwicker, M.: Neural-pull: learning signed distance function from point clouds by learning to pull space onto surface. In: International Conference on Machine Learning, pp. 7246\u20137257 (2021). PMLR"},{"key":"4412_CR25","first-page":"2312","volume":"38","author":"H Huang","year":"2024","unstructured":"Huang, H., Wu, Y., Zhou, J., Gao, G., Gu, M., Liu, Y.-S.: Neusurf: on-surface priors for neural surface reconstruction from sparse input views. Proc. AAAI Conf. Artif. Intell. 38, 2312\u20132320 (2024)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"12","key":"4412_CR26","doi-asserted-by":"publisher","first-page":"7475","DOI":"10.1109\/TPAMI.2024.3392364","volume":"46","author":"J Zhou","year":"2024","unstructured":"Zhou, J., Ma, B., Li, S., Liu, Y.-S., Fang, Y., Han, Z.: CAP-UDF: learning unsigned distance functions progressively from raw point clouds with consistency-aware field optimization. IEEE Trans. Pattern Anal. Mach. Intell. 46(12), 7475\u20137492 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4412_CR27","doi-asserted-by":"crossref","unstructured":"Ren, S., Hou, J., Chen, X., He, Y., Wang, W.: Geoudf: surface reconstruction from 3d point clouds via geometry-guided distance representation. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 14168\u201314178 (2023)","DOI":"10.1109\/ICCV51070.2023.01307"},{"key":"4412_CR28","doi-asserted-by":"crossref","unstructured":"Guillard, B., Stella, F., Fua, P.: Meshudf: fast and differentiable meshing of unsigned distance field networks. In: European Conference on Computer Vision, pp. 576\u2013592 (2022). Springer, Berlin","DOI":"10.1007\/978-3-031-20062-5_33"},{"key":"4412_CR29","doi-asserted-by":"crossref","unstructured":"Hu, J., Li, Y., Hou, F., Hou, J., Zhang, Z., Wang, S., Lei, N., He, Y.: A lightweight UDF learning framework for 3d reconstruction based on local shape functions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1297\u20131307 (2025)","DOI":"10.1109\/CVPR52734.2025.00129"},{"key":"4412_CR30","doi-asserted-by":"crossref","unstructured":"Lu, Y., Want, L., Ding, N., Wang, Y., Shen, S., Cai, S., Gao, L.: Unsigned orthogonal distance fields: an accurate neural implicit representation for diverse 3d shapes. In: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20551\u201320560 (2024)","DOI":"10.1109\/CVPR52733.2024.01942"},{"key":"4412_CR31","unstructured":"Wang, Z., Wang, P., Wang, P., Dong, Q., Gao, J., Chen, S., Xin, S., Tu, C., Wang, W.: Neural-IMLS: Self-supervised implicit moving least-squares network for surface reconstruction. IEEE Trans. Visual. Comput. Graph., 1\u201316 (2023)"},{"key":"4412_CR32","unstructured":"Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp. 11918\u201311930 (2019)"},{"key":"4412_CR33","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"4412_CR34","doi-asserted-by":"crossref","unstructured":"Zhou, L., Du, Y., Wu, J.: 3d shape generation and completion through point-voxel diffusion. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5826\u20135835 (2021)","DOI":"10.1109\/ICCV48922.2021.00577"},{"key":"4412_CR35","doi-asserted-by":"crossref","unstructured":"Luo, S., Hu, W.: Diffusion probabilistic models for 3d point cloud generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2837\u20132845 (2021)","DOI":"10.1109\/CVPR46437.2021.00286"},{"key":"4412_CR36","doi-asserted-by":"crossref","unstructured":"Ren, Z., Kim, M., Liu, F., Liu, X.: Tiger: Time-varying denoising model for 3d point cloud generation with diffusion process. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9462\u20139471 (2024)","DOI":"10.1109\/CVPR52733.2024.00904"},{"key":"4412_CR37","doi-asserted-by":"crossref","unstructured":"Schr\u00f6ppel, P., Wewer, C., Lenssen, J.E., Ilg, E., Brox, T.: Neural point cloud diffusion for disentangled 3d shape and appearance generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8785\u20138794 (2024)","DOI":"10.1109\/CVPR52733.2024.00839"},{"key":"4412_CR38","first-page":"10021","volume":"35","author":"A Vahdat","year":"2022","unstructured":"Vahdat, A., Williams, F., Gojcic, Z., Litany, O., Fidler, S., Kreis, K., et al.: Lion: latent point diffusion models for 3d shape generation. Adv. Neural. Inf. Process. Syst. 35, 10021\u201310039 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"47","key":"4412_CR39","first-page":"1","volume":"23","author":"J Ho","year":"2022","unstructured":"Ho, J., Saharia, C., Chan, W., Fleet, D.J., Norouzi, M., Salimans, T.: Cascaded diffusion models for high fidelity image generation. J. Mach. Learn. Res. 23(47), 1\u201333 (2022)","journal-title":"J. Mach. Learn. Res."},{"key":"4412_CR40","first-page":"36479","volume":"35","author":"C Saharia","year":"2022","unstructured":"Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E.L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al.: Photorealistic text-to-image diffusion models with deep language understanding. Adv. Neural. Inf. Process. Syst. 35, 36479\u201336494 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"4412_CR41","first-page":"12533","volume-title":"Advances in Neural Information Processing Systems","author":"A Sinha","year":"2021","unstructured":"Sinha, A., Song, J., Meng, C., Ermon, S.: D2c: diffusion-decoding models for few-shot conditional generation. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P.S., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol. 34, pp. 12533\u201312548. Curran Associates Inc, Red Hook (2021)"},{"key":"4412_CR42","doi-asserted-by":"crossref","unstructured":"Jeong, M., Kim, H., Cheon, S.J., Choi, B.J., Kim, N.S.: Diff-TTS: a denoising diffusion model for text-to-speech. In: 22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021, pp. 3566\u20133570 (2021). International Speech Communication Association","DOI":"10.21437\/Interspeech.2021-469"},{"key":"4412_CR43","doi-asserted-by":"crossref","unstructured":"Schneider, F., Kamal, O., Jin, Z., Sch\u00f6lkopf, B.: Mo\u00fbsai: efficient text-to-music diffusion models. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 8050\u20138068. Association for Computational Linguistics, Bangkok, Thailand (2024)","DOI":"10.18653\/v1\/2024.acl-long.437"},{"key":"4412_CR44","doi-asserted-by":"crossref","unstructured":"Chowdhury, S., Nag, S., Joseph, K.J., Srinivasan, B.V., Manocha, D.: Melfusion: synthesizing music from image and language cues using diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 26826\u201326835 (2024)","DOI":"10.1109\/CVPR52733.2024.02533"},{"key":"4412_CR45","unstructured":"Lan, Y., Zhou, S., Lyu, Z., Hong, F., Yang, S., Dai, B., Pan, X., Loy, C.C.: Gaussiananything: interactive point cloud latent diffusion for 3d generation. In: ICLR (2025)"},{"key":"4412_CR46","doi-asserted-by":"crossref","unstructured":"Zhu, D., Di, Y., Gavranovic, S., Ilic, S.: Sealion: semantic part-aware latent point diffusion models for 3d generation. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 11789\u201311798 (2025)","DOI":"10.1109\/CVPR52734.2025.01101"},{"issue":"6","key":"4412_CR47","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.cad.2009.01.006","volume":"41","author":"Y Miao","year":"2009","unstructured":"Miao, Y., Pajarola, R., Feng, J.: Curvature-aware adaptive re-sampling for point-sampled geometry. Comput. Aided Des. 41(6), 395\u2013403 (2009)","journal-title":"Comput. Aided Des."},{"issue":"6","key":"4412_CR48","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.gmod.2012.04.012","volume":"74","author":"C Weber","year":"2012","unstructured":"Weber, C., Hahmann, S., Hagen, H., Bonneau, G.-P.: Sharp feature preserving MLS surface reconstruction based on local feature line approximations. Graph. Models 74(6), 335\u2013345 (2012)","journal-title":"Graph. Models"},{"issue":"1","key":"4412_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2421636.2421645","volume":"32","author":"H Huang","year":"2013","unstructured":"Huang, H., Wu, S., Gong, M., Cohen-Or, D., Ascher, U., Zhang, H.: Edge-aware point set resampling. ACM Trans. Graph. (TOG) 32(1), 1\u201312 (2013)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"6","key":"4412_CR50","first-page":"1","volume":"41","author":"R Xu","year":"2022","unstructured":"Xu, R., Wang, Z., Dou, Z., Zong, C., Xin, S., Jiang, M., Ju, T., Tu, C.: RFEPS: reconstructing feature-line equipped polygonal surface. ACM Trans. Graph. (TOG) 41(6), 1\u201315 (2022)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"4412_CR51","doi-asserted-by":"crossref","unstructured":"Yang, X., Ji, D., Li, Y., Xie, J., Guo, J., Guo, Y.: Edgemovingnet: edge-preserving point cloud reconstruction via joint geometry features. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 22150\u201322160 (2025)","DOI":"10.1109\/CVPR52734.2025.02063"},{"key":"4412_CR52","doi-asserted-by":"crossref","unstructured":"Fu, R., Hormann, K., Alliez, P.: LFS-aware surface reconstruction from unoriented 3d point clouds. IEEE Trans. Multimedia (2024)","DOI":"10.1109\/TMM.2024.3453050"},{"key":"4412_CR53","unstructured":"Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F.: ShapeNet: an Information-Rich 3D Model Repository. Technical Report arXiv:1512.03012 [cs.GR], Stanford University \u2013 Princeton University \u2013 Toyota Technological Institute at Chicago (2015)"},{"key":"4412_CR54","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nie\u00dfner, M.: Scannet: Richly-annotated 3d reconstructions of indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5828\u20135839 (2017)","DOI":"10.1109\/CVPR.2017.261"},{"key":"4412_CR55","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhou, S., Park, J.J., Paschalidou, D., You, S., Wetzstein, G., Guibas, L., Kadambi, A.: Alto: alternating latent topologies for implicit 3d reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 259\u2013270 (2023)","DOI":"10.1109\/CVPR52729.2023.00033"},{"key":"4412_CR56","first-page":"3163","volume":"38","author":"S Li","year":"2024","unstructured":"Li, S., Gao, G., Liu, Y., Liu, Y.-S., Gu, M.: Gridformer: point-grid transformer for surface reconstruction. Proc. AAAI Conf. Artif. Intell. 38, 3163\u20133171 (2024)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"3","key":"4412_CR57","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1145\/2487228.2487237","volume":"32","author":"M Kazhdan","year":"2013","unstructured":"Kazhdan, M., Hoppe, H.: Screened Poisson surface reconstruction. ACM Trans. Graph. 32(3), 29 (2013)","journal-title":"ACM Trans. Graph."},{"key":"4412_CR58","doi-asserted-by":"crossref","unstructured":"Li, Z., Sun, W., Govindarajan, S., Xia, S., Rebain, D., Yi, K.M., Tagliasacchi, A.: Noksr: kernel-free neural surface reconstruction via point cloud serialization (2025)","DOI":"10.1109\/3DV66043.2025.00057"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04412-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-026-04412-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04412-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T16:21:39Z","timestamp":1774455699000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-026-04412-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":58,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["4412"],"URL":"https:\/\/doi.org\/10.1007\/s00371-026-04412-2","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"22 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"206"}}