Mr. Katsuya Hotta Profile

Katsuya Hotta

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Proceedings Article | 16 July 2021 Paper

Training PointNet for human point cloud segmentation with 3D meshes

Takuma Ueshima, Katsuya Hotta, Shogo Tokai, Chao Zhang

Proceedings Volume 11794, 117940B (2021) https://doi.org/10.1117/12.2589075

KEYWORDS: Clouds, Data modeling, Neural networks

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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.

Proceedings Article | 13 March 2021 Paper

Affine subspace clustering with nearest subspace neighbor

Katsuya Hotta, Haoran Xie, Chao Zhang

Proceedings Volume 11766, 117661C (2021) https://doi.org/10.1117/12.2590764

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In this paper, we consider the problem of affine subspace clustering, which requires to estimate the corresponding subspaces and assign the corresponding labels to data points on or near a union of low-dimensional affine subspaces. To address this problem, we propose a framework based on Nearest Subspace Neighbor (NSN). NSN is originally designed to estimate the geometric structure of clusters that can not be adequately performed by conventional approaches based on a general distance metric such as K-means, and has been applied in solving the linear subspace clustering problem. However, in real-world scenarios, the vast majority of data exist in the affine subspace rather than linear subspaces. To make better use of NSN, we construct an affinity matrix by incrementally picking the points considering affine subspaces in a greedy fashion. Statistical experiments demonstrate that our method outperforms both the original NSN and an affine subspace clustering method.

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