MVS-STRNet: cross-view space target recognition from multi-view stereo

Wenxi Xie; Xiaoyuan Ren; Canyu Wang; Libing Liang; Zhuang Wang

doi:10.1117/12.3021415

19 February 2024 MVS-STRNet: cross-view space target recognition from multi-view stereo

Wenxi Xie, Xiaoyuan Ren, Canyu Wang, Libing Liang, Zhuang Wang

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130630B (2024) https://doi.org/10.1117/12.3021415
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

In the task of space target recognition, due to the limited conditions of image acquisition, the accumulated train dataset and test dataset have obvious differences in different poses, and the drastic changes of image representation caused by cross-view greatly restrict the performance of space target recognition. In this paper, cross-view space target recognition from multi-view stereo is proposed. The method is based on multi-view stereo to estimate the image depth information, which is fused with texture information of 2D image, and finally uses the 3D CNN to classify the image. The network learns and inferences depth information without the supervision of depth map truth value, which adds 3D information such as geometry of space objects to the classification task, and improves the performance of target recognition. The experimental results on the self-made space target data set prove that the proposed network MVS-STRNet is more efficient on the cross-view data set.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenxi Xie, Xiaoyuan Ren, Canyu Wang, Libing Liang, and Zhuang Wang "MVS-STRNet: cross-view space target recognition from multi-view stereo", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130630B (19 February 2024); https://doi.org/10.1117/12.3021415

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