我们介绍了CpT:Convolutional point Transformer–一种新的深度学习体系结构,用于处理三维点云数据的非结构化性质。CpT是对现有基于注意的卷积神经网络以及以前的3D点云处理变压器的改进。它实现了这一壮举,因为它通过一个卷积投影层(为动态处理局部点集邻域而精心设计)有效地创建了一个新颖而健壮的基于注意的点集嵌入。结...
Flatformer: Flattened window attention for efficient point cloud transformer. In: Proc. IEEE Comput. Vis. Pattern Recognit. (CVPR). pp. 1200–1211. Google Scholar Ma et al., 2021 L. Ma, Y. Li, J. Li, W. Tan, Y. Yu, M.A. Chapman Multi-scale point-wise convolutional neural ...
The proposed backbone uses point-wise separable (PWS) and depth-wise separable convolutions, which are more efficient than standard convolution. The PWS convolution utilizes a residual shortcut link to reduce computation time. We also propose a SFPN that comprises concatenation, transformer encoder-...
Ma, L., Li, Y., Li, J., Tan, W., Yu, Y., Chapman, M.A.: Multi-scale point-wise convolutional neural networks for 3D object segmentation from LiDAR point clouds in large-scale environments. IEEE TITS (2019) Google Scholar
Improving 3D Object Detection with Channel-wise Transformer [det; Github] Voxel-based Network for Shape Completion by Leveraging Edge Generation [completion; Github] Exploring Simple 3D Multi-Object Tracking for Autonomous Driving [tracking] ME-PCN: Point Completion Conditioned on Mask Emptiness [complet...
Fully Point-wise Convolutional Neural Network. Contribute to chaimi2013/FPCNet development by creating an account on GitHub.
convolutional-based(voxel transformer):卷积的attention field较小,限制了长期依赖(感受野小)。作者提出的方法,是一种基于基于Voxel set attention (VSA)方法。 作者解决问题的办法 Voxel-based Set Attention (VSA)(重点) 首先,由于作者还是在点云上应用self-attention,计算量还是比较大,所以作者引入了Set-transformer...
PointTransformer [25] performs self-attention only within local neighborhoods. CloudTransformer [48] inspired by spatial transformer [51], uses an attention mechanism to transform the point cloud into a voxel grid for convolutional operation. Although these works have pro...
to activate the transformer’s strong capability in representing features, we develop a new variant of a multi-head self-attention structure to enhance both point-wise and channel-wise relations of the feature map. In addition, we leverage a positional fusion block to comprehensively capture the ...
Transformer plays an increasingly important role in various computer vision areas and has made remarkable achievements in point cloud analysis. Since existing methods mainly focus on point-wise transformer, an adaptive channel-wise Transformer is propose