9 research outputs found
Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation
The author wishes to extend sincere appreciation to Professor Lin Shi for the generous provision of equipment support, which significantly aided in the successful completion of this research. Furthermore, the author expresses gratitude to Associate Professor Ning Li and Teacher Wei Guan for their invaluable academic guidance and unwavering support. Their expertise and advice played a crucial role in shaping the direction and quality of this research.Peer reviewe
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Cross-Shape Attention for Part Segmentation of 3D Point Clouds
We present a deep learning method that propagates point-wise feature representations across shapes within a collection for the purpose of 3D shape segmentation. We propose a cross-shape attention mechanism to enable interactions between a shape\u27s point-wise features and those of other shapes. The mechanism assesses both the degree of interaction between points and also mediates feature propagation across shapes, improving the accuracy and consistency of the resulting point-wise feature representations for shape segmentation. Our method also proposes a shape retrieval measure to select suitable shapes for cross-shape attention operations for each test shape. Our experiments demonstrate that our approach yields state-of-the-art results in the popular PartNet dataset
Cross-Shape Graph Convolutional Networks
We present a method that processes 3D point clouds by performing graph
convolution operations across shapes. In this manner, point descriptors are
learned by allowing interaction and propagation of feature representations
within a shape collection. To enable this form of non-local, cross-shape graph
convolution, our method learns a pairwise point attention mechanism indicating
the degree of interaction between points on different shapes. Our method also
learns to create a graph over shapes of an input collection whose edges connect
shapes deemed as useful for performing cross-shape convolution. The edges are
also equipped with learned weights indicating the compatibility of each shape
pair for cross-shape convolution. Our experiments demonstrate that this
interaction and propagation of point representations across shapes make them
more discriminative. In particular, our results show significantly improved
performance for 3D point cloud semantic segmentation compared to conventional
approaches, especially in cases with the limited number of training examples
Object-Centric Learning with Capsule Networks : A Survey
The authors would like to thank all reviewers, and especially Professor Chris Williams from the School of Informatics of the University of Edinburgh, who provided constructive feedback and ideas on how to improve this work.Peer reviewe