PDNet: Parallel Dual-branch Network for Point Cloud Geometry Compression and Analysis

Integrating compression with analysis for point clouds poses a formidable challenge due to the inherent tension between the primary goals of compression for a compact representation and analysis for rich semantic retention. To alleviate this gap and maximize the practical requirements, we introduce a Parallel Dual-branch Network (PDNet) for lossy point cloud geometry compression, whose outputs are also analysis-friendly. The proposed method uses a novel Transformer-based encoder-decoder framework to incorporate local and global attention for point cloud latent representation computation. Specifically, the encoder comprises a Multi-scale Local-Global Feature extraction (MLGF) block to capture compact local and global latent features. The decoding and the hyper-prior modules employ a Transformer with No Position Embedding (TNPE) block and a Multilayer Perceptron (MLP) layer to reconstruct point clouds accurately. Furthermore, our method allows simultaneous point cloud analysis based on the compressed bitstream, such as point cloud classification. Experimental results demonstrate that our PDNet achieves nearly a 40% BD-Rate gain compared to G-PCC and other point-based compression counterparts. Besides, a 26% accuracy improvement in instance classification is observed compared to reconstructed point cloud classification.

Integrating compression with analysis for point clouds poses a formidable challenge due to the inherent tension between the primary goals of compression for a compact representation and analysis for rich semantic retention. To alleviate this gap and maximize the practical requirements, we introduce a Parallel Dual-branch Network (PDNet) for lossy point cloud geometry compression, whose outputs are also analysis-friendly. The proposed method uses a novel Transformer-based encoder-decoder framework to incorporate local and global attention for point cloud latent representation computation. Specifically, the encoder comprises a Multi-scale Local-Global Feature extraction (MLGF) block to capture compact local and global latent features. The decoding and the hyper-prior modules employ a Transformer with No Position Embedding (TNPE) block and a Multilayer Perceptron (MLP) layer to reconstruct point clouds accurately. Furthermore, our method allows simultaneous point cloud analysis based on the compressed bitstream, such as point cloud classification. Experimental results demonstrate that our PDNet achieves nearly a 40% BD-Rate gain compared to G-PCC and other point-based compression counterparts. Besides, a 26% accuracy improvement in instance classification is observed compared to reconstructed point cloud classification.