{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:40Z","timestamp":1743033640949,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031728471"},{"type":"electronic","value":"9783031728488"}],"license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"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\/978-3-031-72848-8_2","type":"book-chapter","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T13:35:15Z","timestamp":1732800915000},"page":"19-35","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Frugal 3D Point Cloud Model Training via\u00a0Progressive Near Point Filtering and\u00a0Fused Aggregation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6925-8824","authenticated-orcid":false,"given":"Donghyun","family":"Lee","sequence":"first","affiliation":[]},{"given":"Yejin","family":"Lee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4266-4919","authenticated-orcid":false,"given":"Jae W.","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Hongil","family":"Yoon","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"2_CR1","unstructured":"Quickfps. http:\/\/github.com\/hanm2019\/bucket-based_farthest-point-sampling_GPU"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Armeni, I., et al.: 3D semantic parsing of large-scale indoor spaces. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.170"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Choy, C., Gwak, J., Savarese, S.: 4D spatio-temporal convnets: minkowski convolutional neural networks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00319"},{"key":"2_CR4","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: CVPR (2017)","DOI":"10.1109\/CVPR.2017.261"},{"key":"2_CR5","unstructured":"Dao, T., Fu, D.Y., Ermon, S., Rudra, A., R\u00e9, C.: FlashAttention: fast and memory-efficient exact attention with IO-awareness. In: NeurIPS (2022)"},{"key":"2_CR6","unstructured":"Evci, U., Gale, T., Menick, J., Castro, P.S., Elsen, E.: Rigging the lottery: making all tickets winners. In: Proceedings of the 37th International Conference on Machine Learning (ICML) (2020)"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Fan, L., et al.: Embracing single stride 3D object detector with sparse transformer. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00827"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Feng, Y., Hammonds, G., Gan, Y., Zhu, Y.: Crescent: taming memory irregularities for accelerating deep point cloud analytics. In: Proceedings of the 49th Annual International Symposium on Computer Architecture (ISCA) (2022)","DOI":"10.1145\/3470496.3527395"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Feng, Y., Tian, B., Xu, T., Whatmough, P., Zhu, Y.: Mesorasi: architecture support for point cloud analytics via delayed-aggregation. In: Proceedings of the 53th International Symposium on Microarchitecture (MICRO) (2020)","DOI":"10.1109\/MICRO50266.2020.00087"},{"key":"2_CR10","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Graham, B., Engelcke, M., van\u00a0der Maaten, L.: 3D semantic segmentation with submanifold sparse convolutional networks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00961"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Han, M., et\u00a0al.: Quickfps: Architecture and algorithm co-design for farthest point sampling in large-scale point clouds. IEEE Trans. Comput.-Aided Design Integr. Circuits Syst. (2023)","DOI":"10.1109\/TCAD.2023.3274922"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Hu, Q., et al: RandLA-Net: efficient semantic segmentation of large-scale point clouds. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"2_CR14","unstructured":"Junyuan\u00a0Ouyang, Xiao\u00a0Liu, H.C.: Hierarchical adaptive voxel-guided sampling for real-time applications in large-scale point clouds. arXiv preprint arXiv:2305.14306 (2023)"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Le, E.T., Kokkinos, I., Mitra, N.J.: Going deeper with lean point networks. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00952"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Li, J., Zhou, J., Xiong, Y., Chen, X., Chakrabarti, C.: An adjustable farthest point sampling method for approximately-sorted point cloud data. In: 2022 IEEE Workshop on Signal Processing Systems (SiPS) (2022)","DOI":"10.1109\/SiPS55645.2022.9919246"},{"key":"2_CR17","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: convolution on X-transformed points. In: NeurIPS (2018)"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Lin, H., Zheng, X., Li, L., Chao, F., Wang, S., Wang, Y., Tian, Y., Ji, R.: Meta architecture for point cloud analysis. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01696"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Meng, G., Lu, J., Xiang, S., Pan, C.: DensePoint: learning densely contextual representation for efficient point cloud processing. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00534"},{"key":"2_CR20","unstructured":"Liu, Z., Tang, H., Lin, Y., Han, S.: Point-voxel CNN for efficient 3D deep learning. In: NeurIPS (2019)"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yang, X., Tang, H., Yang, S., Han, S.: FlatFormer: flattened window attention for efficient point cloud transformer. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00122"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Nekrasov, A., Schult, J., Litany, O., Leibe, B., Engelmann, F.: Mix3D: out-of-context data augmentation for 3D scenes. In: International Conference on 3D Vision (3DV) (2021)","DOI":"10.1109\/3DV53792.2021.00022"},{"key":"2_CR23","unstructured":"NVIDIA geforce RTX 3090 (2020). https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/30-series\/rtx-3090\/"},{"key":"2_CR24","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: NeurIPS (2019)"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum pointnets for 3D object detection from RGB-D data. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00102"},{"key":"2_CR26","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. arXiv preprint arXiv:1612.00593 (2016)"},{"key":"2_CR27","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. arXiv preprint arXiv:1706.02413 (2017)"},{"key":"2_CR28","unstructured":"Qian, G., Hammoud, H., Li, G., Thabet, A., Ghanem, B.: ASSANet: an anisotropical separable set abstraction for efficient point cloud representation learning. In: NeurIPS (2021)"},{"key":"2_CR29","unstructured":"Qian, G., et al.: PointNext: revisiting PointNet++ with improved training and scaling strategies. In: NeurIPS (2022)"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: incremental classifier and representation learning. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.587"},{"key":"2_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/978-3-030-58604-1_41","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Tang","year":"2020","unstructured":"Tang, H., et al.: Searching efficient 3D architectures with sparse point-voxel convolution. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12373, pp. 685\u2013702. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58604-1_41"},{"key":"2_CR32","unstructured":"Wang, M., et al.: Deep graph library: a graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315 (2019)"},{"key":"2_CR33","doi-asserted-by":"crossref","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) (2019)","DOI":"10.1145\/3326362"},{"key":"2_CR34","unstructured":"Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: CVPR (2015)"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Xu, Q., Sun, X., Wu, C.Y., Wang, P., Neumann, U.: Grid-GCN for fast and scalable point cloud learning (2020)","DOI":"10.1109\/CVPR42600.2020.00570"},{"key":"2_CR36","unstructured":"Yang, Y.Q., et al.: Swin3D: a pretrained transformer backbone for 3D indoor scene understanding. arXiv preprint arXiv:2304.06906 (2023)"},{"key":"2_CR37","doi-asserted-by":"crossref","unstructured":"Ying, Z., Bhuyan, S., Kang, Y., Zhang, Y., Kandemir, M.T., Das, C.R.: EdgePC: efficient deep learning analytics for point clouds on edge devices. In: Proceedings of the 50th Annual International Symposium on Computer Architecture (ISCA) (2023)","DOI":"10.1145\/3579371.3589113"},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, J.F., Zhang, Z.: Point-X: a spatial-locality-aware architecture for energy-efficient graph-based point-cloud deep learning. In: Proceedings of the 54th International Symposium on Microarchitecture (MICRO) (2021)","DOI":"10.1145\/3466752.3480081"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Zhu, X., et al.: Cylindrical and asymmetrical 3D convolution networks for LiDAR segmentation. arXiv preprint arXiv:2011.10033 (2020)","DOI":"10.1109\/CVPR46437.2021.00981"}],"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\/978-3-031-72848-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T14:04:19Z","timestamp":1732802659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72848-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"ISBN":["9783031728471","9783031728488"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72848-8_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,29]]},"assertion":[{"value":"29 November 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"}}]}}