LRA-Net: local region attention network for 3D point cloud completion

Hang Wu; Yubin Miao

doi:10.1117/12.2586422

4 January 2021 LRA-Net: local region attention network for 3D point cloud completion

Hang Wu, Yubin Miao

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 1160519 (2021) https://doi.org/10.1117/12.2586422
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

Incomplete Point clouds obtained from one-side scanning always result in structural loss in 3D shape representations, thus many learning-based methods are proposed to restore complete point clouds from partial ones. However, most of them only utilize global features of inputs to generate outputs, which might lose details. In this paper, a new method that utilizes both global and local features is proposed. First, Local features are extracted from inputs and analyzed under the conditions interpreted by global features. Second, conditional local feature vectors are deeply fused with each other via graph convolution and self-attention. Third, deeply-fused features are decoded for generating coarse point clouds. Last, global features extracted from inputs and coarse outputs are combined to generate fine outputs with high-density. Our network is trained and tested on eight categories of objects in ModelNet. The results show that our network is able to overcome instability in local feature awareness, restore complete point clouds with more details and smoother shapes, and outperform most of those existing methods both intuitively and quantitatively. Our source codes will be available at: https://github.com/wuhang100/LRA-Net.

Citation Download Citation

Hang Wu and Yubin Miao "LRA-Net: local region attention network for 3D point cloud completion", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 1160519 (4 January 2021); https://doi.org/10.1117/12.2586422

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