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 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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Publication Details

Journal
SICE Journal of Control Measurement and System Integration
Published
2025-05-03
DOI
https://doi.org/10.1080/18824889.2025.2497600
Citations
3
Primary Topic
3D Surveying and Cultural Heritage
Type
article
Field-Weighted Citation Impact
4.69
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article

Comparative verification of 3D reconstructed point cloud data: comparison of AI estimation and multi-view stereo methods using Nerfstudio and Metashape

Atsushi Hayashi, Yuta Tsukamoto, Nobuo Kochi, Kenichi Tokuda
3 citations
SICE Journal of Control Measurement and System Integration
3D Surveying and Cultural Heritage
4.69
article

Comparative verification of 3D reconstructed point cloud data: comparison of AI estimation and multi-view stereo methods using Nerfstudio and Metashape

Atsushi Hayashi, Yuta Tsukamoto, Nobuo Kochi, Kenichi Tokuda
article en
3 citations

Abstract

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.

SICE Journal of Control Measurement and System IntegrationVol. 18(1)
National Agriculture and Food Research Organization (JP), Shanghai Institute for Science of Science (CN), Institute of Science Tokyo (JP), Chuo University (JP)
Openalex Percentile: Top 7%
3D Surveying and Cultural Heritage
4.69
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Comparative verification of 3D reconstructed point cloud data: comparison of AI estimation and multi-view stereo methods using Nerfstudio and Metashape — Atsushi Hayashi, Yuta Tsukamoto, et al. · SICE Journal of Control Measurement and System Integration (2025) | TGRS Research Map | TGRS