Dr. Hang Wu Profile

Dr. Hang Wu

at Shanghai Jiao Tong Univ

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Publications (2)

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Proceedings Article | 3 April 2024 Paper

Point scene understanding via points-to-mesh reconstruction and multi-level utilization of proposals

Mengxiang Hao, Hang Wu, Ruchong Fu, Yubin Miao

Proceedings Volume 13072, 130720R (2024) https://doi.org/10.1117/12.3023451

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Semantic scene reconstruction from sparse and incomplete point clouds is a critical task for point scene understanding. It aims to recognize semantic labels for objects and recover their complete shapes as meshes. Existing methods often fail to realize high-quality instance reconstruction due to inadequate shape representation and underutilization of proposal point clouds. To address these issues, we optimize the previous BSP/occupancy-to-mesh reconstruction framework to points-to-mesh and accomplish multi-level utilization of proposals. We chose point cloud as the representation of completion to reduce the difficulty of restoring curved shallow parts. Benefiting from the optimization, we can match and merge proposal point clouds with the restored ones, avoiding missing parts existing in inputs. We design an effective pose normalization module to extract point-based features from normalized proposals, which are fused with features extracted from voxelized proposals, avoiding the detailed geometry lost in voxelization and enhancing the reconstruction's robustness to different input postures. The suitable points-to-mesh reconstruction framework and full utilization of proposals make our method improve reconstruction results efficiently. Detailed experiments on the challenging ScanNet dataset of the semantic scene reconstruction benchmark show that our network outperforms state-of-the-art methods in both completion and mapping metrics.

Proceedings Article | 4 January 2021 Paper

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

Hang Wu, Yubin Miao

Proceedings Volume 11605, 1160519 (2021) https://doi.org/10.1117/12.2586422

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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.

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