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Efficient Outdoor 3D Point Cloud Semantic Segmentation for Critical Road Objects and Distributed Contexts
Large-scale point cloud semantic understanding is an important problem in self-driving cars and autonomous robotics navigation.
Heat Diffusion Based Multi-Scale and Geometric Structure-Aware Transformer for Mesh Segmentation
A novel Transformer model called MeshFormer is proposed, which i) integrates Heat Diffusion method into Multi-head Self-Attention operation (HDMSA) to adaptively capture the features from local neighborhood to global contexts; ii) applies a novel Heat Kernel Signature based Structure Encoding (HKSSE) to embed the intrinsic geometric structures of mesh instances into Transformer for structure-aware processing.
Persistent Homology Based Graph Convolution Network for Fine-Grained 3D Shape Segmentation
Current deep learning and graph machine learning methods fail to tackle such challenges and thus provide inferior performance in fine-grained 3D analysis.
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