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05/10/2023
D-Net: Learning for Distinctive Point Clouds by Self-Attentive Point Searching and Learnable Feature Fusion
Learning and selecting important points on a point cloud is crucial for ...
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12/08/2020
SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization
The task of point cloud upsampling aims to acquire dense and uniform poi...
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05/26/2020
Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions
Fine-grained 3D shape classification is important and research challengi...
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03/18/2020
LRC-Net: Learning Discriminative Features on Point Clouds by Encoding Local Region Contexts
Learning discriminative feature directly on point clouds is still challe...
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03/18/2020
LRC-Net: Learning Discriminative Features on Point Clouds by EncodingLocal Region Contexts
Learning discriminative feature directly on point clouds is still challe...
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08/29/2019
Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsules
Learning discriminative shape representation directly on point clouds is...
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08/02/2019
L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention
Auto-encoder is an important architecture to understand point clouds in ...
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05/18/2019
Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views
Deep learning has achieved remarkable results in 3D shape analysis by le...
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11/06/2018