  Learning-based methods have proven successful in compressing geometric
information for point clouds. For attribute compression, however, they still
lag behind non-learning-based methods such as the MPEG G-PCC standard. To
bridge this gap, we propose a novel deep learning-based point cloud attribute
compression method that uses a generative adversarial network (GAN) with sparse
convolution layers. Our method also includes a module that adaptively selects
the resolution of the voxels used to voxelize the input point cloud. Sparse
vectors are used to represent the voxelized point cloud, and sparse
convolutions process the sparse tensors, ensuring computational efficiency. To
the best of our knowledge, this is the first application of GANs to compress
point cloud attributes. Our experimental results show that our method
outperforms existing learning-based techniques and rivals the latest G-PCC test
model (TMC13v23) in terms of visual quality.
