Learning-Based Point Cloud Decoding with Independent and Scalable Reduced Complexity

Point Clouds (PCs) have gained significant attention due to their usage in diverse application domains, notably virtual and augmented reality. While PCs excel in providing detailed 3D visualization, this typically requires millions of points which must be efficiently coded for real-world deployment, notably storage and streaming. Recently, learning-based coding solutions have been adopted, notably in the JPEG Pleno Point Coding (PCC) standard, which uses a coding model with millions of model parameters. This requires the use of high-performance computing devices, which may not be available, notably at the decoder side. In this context, this paper proposes two reduced complexity decoding solutions, based on the adoption of scalability principles, to decode the same JPEG PCC compliant bitstreams. The design of these solutions is based on two innovative model pruning strategies which reduce the decoding complexity. The experimental results demonstrate the effective capability to significantly reduce the number of decoding model parameters with an acceptable penalty on Rate-Distortion (RD) performance compared to the full complexity model.