SPoVT: Semantic Prototype Variational Transformer for Dense Point Cloud Semantic Completion

2022-11-15 · Advances in Neural Information Processing Systems 35 2022 11 · Sheng-Yu Huang, Hao-Yu Hsu, Frank Wang

Point cloud completion is an active research topic for 3D vision and has been widely studied in recent years. Instead of directly predicting the missing point cloud from the partial input, we introduce a Semantic-Prototype Variational Transformer (SPoVT) in this work, which takes both partial point cloud and their semantic labels as the inputs for semantic point cloud object completion. By observing and attending to geometry and semantic information as input features, our SPoVT would derive point cloud features and their semantic prototypes for completion purposes. As a result, our SPoVT not only performs point cloud completion with varying resolution, it also allows manipulation of different semantic parts of an object. Experiments on benchmark datasets would quantitatively and qualitatively verify the effectiveness and practicality of our proposed model.

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Code (1)

haoyuhsu/spovt pytorch

Tasks

Point Cloud CompletionPoint Cloud Semantic Completion

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