Adaptive Geometry Partition for Point Cloud Compression

Abstract

Octree (OT) geometry partitioning has been acknowledged as an efficient representation in state-of-the-art point cloud compression (PCC) schemes. In this work, an adaptive geometry partition and coding scheme is proposed to improve the OT based coding framework. First, quad-tree (QT) and binary-tree (BT) partitions are introduced as alternative geometry partition modes for the first time under the context of OT-based point cloud compression. The adaptive geometry partition scheme enables flexible three-dimensional (3D) space representations and higher coding efficiency. However, exhaustive searching for the optimal partition from all possible combinations of OT, QT and BT is impractical because the entire search space could be huge. Therefore, two hyper-parameters are introduced to specify the conditions on which QT and BT partitions will be applied. Once the two parameters are determined, the partition mode can be derived according to the geometry shape of current coding node. To investigate the impact of different partition combinations on the coding gains, we conduct thorough mathematical and experimental analyses. Based on the analyses, an adaptive parameter selection scheme is presented to optimize the coding efficiency adaptively, where multi-resolution features are extracted from the partition pyramid and a decision tree model is trained for the optimal hyper-parameters. The proposed adaptive geometry partition scheme has shown significant coding gains, and it has been adopted in the state-of-the-art MPEG Geometry based PCC (G-PCC) standard. For the sparser point clouds, the bit savings are up to 10.8% and 3.5% for lossy and lossless geometry coding without significant complexity increment.