| Résumé: | Point cloud is a versatile geometric representation that could be applied in computer vision tasks. On account of the disorder of point cloud, it is challenging to design a deep neural network used in point cloud analysis. Furthermore, most existing frameworks for point cloud processing either hardly consider the local neighboring information or ignore context-aware and spatially-aware features. To deal with the above problems, we propose a novel point cloud processing architecture named TR-Net, which is based on transformer. This architecture reformulates the point cloud processing task as a set-to-set translation problem. TR-Net directly operates on raw point clouds without any data transformation or annotation, which reduces the consumption of computing resources and memory usage. Firstly, a neighborhood embedding backbone is designed to effectively extract the local neighboring information from point cloud. Then, an attention-based sub-network is constructed to better learn a semantically abundant and discriminatory representation from embedded features. Finally, effective global features are yielded through feeding the features extracted by attention-based sub-network into a residual backbone. For different downstream tasks, we build different decoders. Extensive experiments on the public datasets illustrate that our approach outperforms other state-of-the-art methods. For example, our TR-Net performs 93.1% overall accuracy on the ModelNet40 dataset and the TR-Net archives a mIou of 85.3% on the ShapeNet dataset for part segmentation.
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