Comparative verification of 3D reconstructed point cloud data: comparison of AI estimation and multi-view stereo methods using Nerfstudio and Metashape
We investigated the feasibility of using a 3D point cloud estimated by a neural radiance field (NeRF). We evaluated the quality of point clouds reconstructed with NeRF and multi-view stereo. The point cloud reconstructed by NeRF contains substantial noise and shows a wavy surface even on a flat surf...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/18824889.2025.2497600 |
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| Summary: | We investigated the feasibility of using a 3D point cloud estimated by a neural radiance field (NeRF). We evaluated the quality of point clouds reconstructed with NeRF and multi-view stereo. The point cloud reconstructed by NeRF contains substantial noise and shows a wavy surface even on a flat surface. Therefore, a point cloud generated by NeRF is not appropriate as a highly accurate 3D model with the size and shape of an actual object. However, it is effective for applications that do not require highly accurate point clouds, such as the detection and 3D modelling of objects with few feature-point surfaces or optically transparent objects, which have been difficult to achieve using conventional methods. |
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| ISSN: | 1884-9970 |