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DV-Det: Efficient 3D Point Cloud Object Detection with Dynamic Voxelization
To achieve this goal, we propose dynamic voxelization, a method that voxellizes points at local scale on-the-fly.
DV-ConvNet: Fully Convolutional Deep Learning on Point Clouds with Dynamic Voxelization and 3D Group Convolution
3D point cloud interpretation is a challenging task due to the randomness and sparsity of the component points.
Is Discriminator a Good Feature Extractor?
From this perspective and combined with further analyses, we found that to avoid mode collapse, the features extracted by the discriminator are not guided to be different for the real samples, but divergence without noise is indeed allowed and occupies a large proportion of the feature space.
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