7 citations · 7 across the 1 of their papers we have counts for
6 papers
Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions
Xinhai Liu, Zhizhong Han, Yu-Shen Liu +1
Fine-grained 3D shape classification is important for shape understanding and analysis, which poses a challenging research problem. However, the studies on the fine-grained 3D shap…
LRC-Net: Learning Discriminative Features on Point Clouds by Encoding Local Region Contexts
Xinhai Liu, Zhizhong Han, Fangzhou Hong +2
Learning discriminative feature directly on point clouds is still challenging in the understanding of 3D shapes. Recent methods usually partition point clouds into local region set…
Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsules
Xin Wen, Zhizhong Han, Xinhai Liu +1
Learning discriminative shape representation directly on point clouds is still challenging in 3D shape analysis and understanding. Recent studies usually involve three steps: first…
L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention
Xinhai Liu, Zhizhong Han, Xin Wen +2
Auto-encoder is an important architecture to understand point clouds in an encoding and decoding procedure of self reconstruction. Current auto-encoder mainly focuses on the learni…
Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views
Zhizhong Han, Xinhai Liu, Yu-Shen Liu +1
Deep learning has achieved remarkable results in 3D shape analysis by learning global shape features from the pixel-level over multiple views. Previous methods, however, compute lo…
Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network
Xinhai Liu, Zhizhong Han, Yu-Shen Liu +1
Exploring contextual information in the local region is important for shape understanding and analysis. Existing studies often employ hand-crafted or explicit ways to encode contex…