FPNforV-PCC

This dataset is for paper FPN-based V-PCC Fast CU Partition Approach, and is what the authors use for training the FPN mentioned in the paper. The CU partitioning data in this dataset comes from V-PCC using VVC to make CU partition and extracting data for a sample based on 64×64 CU. Encoder configuration when extracting data as described in Section III-A of the paper, the point cloud source is the first 32 frames of basketball_player order by owlii, including the partitioning of 5 different QPs. In the dataset, AttrI_TOri and GeomP_TOri represents the input for luma values, AttrI_Resi and GeomP_Resi stands for the prediction residual input, AttrI_Occu and GeomP_Occu denotes the placeholder information input, and AttrI_y_QP and GeomP_y_QP encompasses both the label value and QP input.