Training PointNet for human point cloud segmentation with 3D meshes

Takuma Ueshima; Katsuya Hotta; Shogo Tokai; Chao Zhang

doi:10.1117/12.2589075

16 July 2021 Training PointNet for human point cloud segmentation with 3D meshes

Takuma Ueshima, Katsuya Hotta, Shogo Tokai, Chao Zhang

Proceedings Volume 11794, Fifteenth International Conference on Quality Control by Artificial Vision; 117940B (2021) https://doi.org/10.1117/12.2589075
Event: Fifteenth International Conference on Quality Control by Artificial Vision, 2021, Tokushima, Japan

Abstract

PointNet, which enables end-to-end learning for scattered/unordered point data, is a popular neural network architecture. However, in many applications, large amounts of complete point clouds are hardly available for non-rigid objects such as the human body. To generate the training data of PointNet, in this study, we propose to generate human body point clouds of various postures by uniformly sampling point clouds from meshes with respect to multiple human mesh model datasets. Experiments show that the model trained with the point clouds generated from mesh data is effective in the task of human body segmentation.

Citation Download Citation

Takuma Ueshima, Katsuya Hotta, Shogo Tokai, and Chao Zhang "Training PointNet for human point cloud segmentation with 3D meshes", Proc. SPIE 11794, Fifteenth International Conference on Quality Control by Artificial Vision, 117940B (16 July 2021); https://doi.org/10.1117/12.2589075

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