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Rotation invariance and equivariance in 3D deep learning: a survey
Deep neural networks (DNNs) in 3D scenes show a strong capability of extracting high-level
semantic features and significantly promote research in the 3D field. 3D shapes and scenes …
semantic features and significantly promote research in the 3D field. 3D shapes and scenes …
Robust 3D shape classification via non-local graph attention network
We introduce a non-local graph attention network (NLGAT), which generates a novel global
descriptor through two sub-networks for robust 3D shape classification. In the first sub …
descriptor through two sub-networks for robust 3D shape classification. In the first sub …
Incorporating rotation invariance with non-invariant networks for point clouds
Rotation invariance is a fundamental requirement of point cloud processing when input point
clouds are not aligned. Many non-invariant networks performing well on aligned point …
clouds are not aligned. Many non-invariant networks performing well on aligned point …
Self-supervised rotation-equivariant spherical vector network for learning canonical 3D point cloud orientation
H Chen, J Zhao, K Chen, Y Chen - Engineering Applications of Artificial …, 2024 - Elsevier
The perception of orientation in augmented reality, robot gras**, and 3D scene
understanding is commonly addressed through the utilization of hand-crafted geometric …
understanding is commonly addressed through the utilization of hand-crafted geometric …
Tsi-Gcn: Translation and Scaling Invariant Gcn for 3d Point Cloud Analysis
Point cloud is a crucial data format for 3D vision, but its irregularity makes it challenging to
comprehend the associated geometric information. Although some previous research has …
comprehend the associated geometric information. Although some previous research has …
[PDF][PDF] Supplementary Materials for Robust 3D Shape Classification via Non-local Graph Attention Network
S Qin, Z Li, L Liu - openaccess.thecvf.com
Theorem 1. For any two points xi and xj on the point cloud model, their neighborhood
matrices are **s, Xjs, and their Gram matrices are G (**s), G (Xjs), respectively. If G (**s) and …
matrices are **s, Xjs, and their Gram matrices are G (**s), G (Xjs), respectively. If G (**s) and …