Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility

• URL: http://arxiv.org/abs/2108.08378v1
• Date: Wed, 18 Aug 2021 20:28:16 GMT
• Title: Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility
• Authors: Shuang Song, Zhaopeng Cui and Rongjun Qin
• Abstract summary: We present a novel framework for mesh reconstruction from unstructured point clouds. We take advantage of the learned visibility of the 3D points in the virtual views and traditional graph-cut based mesh generation.
• Score: 17.929307870456416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel framework for mesh reconstruction from unstructured point clouds by taking advantage of the learned visibility of the 3D points in the virtual views and traditional graph-cut based mesh generation. Specifically, we first propose a three-step network that explicitly employs depth completion for visibility prediction. Then the visibility information of multiple views is aggregated to generate a 3D mesh model by solving an optimization problem considering visibility in which a novel adaptive visibility weighting in surface determination is also introduced to suppress line of sight with a large incident angle. Compared to other learning-based approaches, our pipeline only exercises the learning on a 2D binary classification task, \ie, points visible or not in a view, which is much more generalizable and practically more efficient and capable to deal with a large number of points. Experiments demonstrate that our method with favorable transferability and robustness, and achieve competing performances \wrt state-of-the-art learning-based approaches on small complex objects and outperforms on large indoor and outdoor scenes. Code is available at https://github.com/GDAOSU/vis2mesh.

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