CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance

• URL: http://arxiv.org/abs/2203.09887v1
• Date: Fri, 18 Mar 2022 11:50:25 GMT
• Title: CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance
• Authors: Tianchen Zhao, Niansong Zhang, Xuefei Ning, He Wang, Li Yi, Yu Wang
• Abstract summary: We propose CodedVTR (Codebook-based Voxel TRansformer) for 3D sparse voxel transformers. On the one hand, we propose the codebook-based attention that projects an attention space into its subspace represented by the combination of "prototypes" in a learnable codebook. On the other hand, we propose geometry-aware self-attention that utilizes geometric information (geometric pattern, density) to guide attention learning.
• Score: 22.39628991021092
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Transformers have gained much attention by outperforming convolutional neural networks in many 2D vision tasks. However, they are known to have generalization problems and rely on massive-scale pre-training and sophisticated training techniques. When applying to 3D tasks, the irregular data structure and limited data scale add to the difficulty of transformer's application. We propose CodedVTR (Codebook-based Voxel TRansformer), which improves data efficiency and generalization ability for 3D sparse voxel transformers. On the one hand, we propose the codebook-based attention that projects an attention space into its subspace represented by the combination of "prototypes" in a learnable codebook. It regularizes attention learning and improves generalization. On the other hand, we propose geometry-aware self-attention that utilizes geometric information (geometric pattern, density) to guide attention learning. CodedVTR could be embedded into existing sparse convolution-based methods, and bring consistent performance improvements for indoor and outdoor 3D semantic segmentation tasks

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