[2005.02138] PointTriNet: Learned Triangulation of 3D Point Setsopen searchopen navigation menucontact arXivsubscribe to arXiv mailings

This work considers a new task in geometric deep learning: generating a triangulation among a set of points in 3D space. We present PointTriNet, a differentiable and scalable approach enabling point set triangulation as a layer in 3D learning pipelines. The method iteratively applies two neural networks to generate a triangulation: a classification network predicts whether a candidate triangle should appear in the triangulation, while a proposal network suggests additional candidates. Both networks are structured as PointNets over nearby points and triangles, using a novel triangle-relative input encoding. Since these learning problems operate on local geometric data, our method scales effectively to large input sets and unseen shape categories, and we can train the networks in an unsupervised manner from a collection of shapes represented as meshes or point clouds. We demonstrate the effectiveness of this approach for classical meshing tasks, robustness to outliers, and as a component

2 mentions: @slam_hub
Date: 2020/06/28 06:52

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@slam_hub PointNetに類似したネットワークを用い点群から三角形メッシュを生成.入力点群から三角形群を出力するネットと,入力三角形群の中から3Dモデルとして妥当な三角形を判定するネットを交互に適用し,メッシュモデルを復元する. t.co/QVv7vmysPt t.co/8JeOljgxXg

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