Learnable Point Cloud Sampling Considering Seed Point for Neural Surface Reconstruction
Reconstruction of surfaces from point clouds is essential in numerous practical applications. An approach in which neural fields are trained as surface representations from point clouds has garnered significant interest in recent years. However, these techniques present scalability issues to large s...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10804151/ |
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| author | Kohei Matsuzaki Keisuke Nonaka |
| author_facet | Kohei Matsuzaki Keisuke Nonaka |
| author_sort | Kohei Matsuzaki |
| collection | DOAJ |
| description | Reconstruction of surfaces from point clouds is essential in numerous practical applications. An approach in which neural fields are trained as surface representations from point clouds has garnered significant interest in recent years. However, these techniques present scalability issues to large scenes since they are limited in the size of point clouds that can be processed. This work proposes a learnable point cloud sampling designed to address the scalability issues. We introduce a sampling network that considers a seed point acting as the origin to sample points from a part of the scene. The seed point is one of the input points that is selected in a spatially uniform manner. This prompts a surface reconstruction network to learn the detailed geometry on partial regions of the scene. We also propose a training pipeline based on point cloud splitting and merging to avoid an increase in the memory footprint. We jointly train the sampling network and surface reconstruction network using a task loss to optimize the sampling network for the surface reconstruction task. Experimental results on scene-level datasets captured from real-world environments demonstrate that our method performs better than state-of-the-art methods in surface reconstruction. |
| format | Article |
| id | doaj-art-1633107d330b46aabc2fb342a6dedfce |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1633107d330b46aabc2fb342a6dedfce2025-08-20T01:57:00ZengIEEEIEEE Access2169-35362024-01-011219094519095810.1109/ACCESS.2024.351862010804151Learnable Point Cloud Sampling Considering Seed Point for Neural Surface ReconstructionKohei Matsuzaki0https://orcid.org/0000-0001-9386-2192Keisuke Nonaka1https://orcid.org/0000-0002-9701-28623D Space Transmission Laboratory, KDDI Research, Inc., Fujimino, Saitama, Japan3D Space Transmission Laboratory, KDDI Research, Inc., Fujimino, Saitama, JapanReconstruction of surfaces from point clouds is essential in numerous practical applications. An approach in which neural fields are trained as surface representations from point clouds has garnered significant interest in recent years. However, these techniques present scalability issues to large scenes since they are limited in the size of point clouds that can be processed. This work proposes a learnable point cloud sampling designed to address the scalability issues. We introduce a sampling network that considers a seed point acting as the origin to sample points from a part of the scene. The seed point is one of the input points that is selected in a spatially uniform manner. This prompts a surface reconstruction network to learn the detailed geometry on partial regions of the scene. We also propose a training pipeline based on point cloud splitting and merging to avoid an increase in the memory footprint. We jointly train the sampling network and surface reconstruction network using a task loss to optimize the sampling network for the surface reconstruction task. Experimental results on scene-level datasets captured from real-world environments demonstrate that our method performs better than state-of-the-art methods in surface reconstruction.https://ieeexplore.ieee.org/document/10804151/Point cloud samplingsurface reconstructionneural fieldsseed pointlocal geometry |
| spellingShingle | Kohei Matsuzaki Keisuke Nonaka Learnable Point Cloud Sampling Considering Seed Point for Neural Surface Reconstruction IEEE Access Point cloud sampling surface reconstruction neural fields seed point local geometry |
| title | Learnable Point Cloud Sampling Considering Seed Point for Neural Surface Reconstruction |
| title_full | Learnable Point Cloud Sampling Considering Seed Point for Neural Surface Reconstruction |
| title_fullStr | Learnable Point Cloud Sampling Considering Seed Point for Neural Surface Reconstruction |
| title_full_unstemmed | Learnable Point Cloud Sampling Considering Seed Point for Neural Surface Reconstruction |
| title_short | Learnable Point Cloud Sampling Considering Seed Point for Neural Surface Reconstruction |
| title_sort | learnable point cloud sampling considering seed point for neural surface reconstruction |
| topic | Point cloud sampling surface reconstruction neural fields seed point local geometry |
| url | https://ieeexplore.ieee.org/document/10804151/ |
| work_keys_str_mv | AT koheimatsuzaki learnablepointcloudsamplingconsideringseedpointforneuralsurfacereconstruction AT keisukenonaka learnablepointcloudsamplingconsideringseedpointforneuralsurfacereconstruction |