Learned Compression in Adaptive Point Cloud Streaming: Opportunities, Challenges and Limitations

Michael Rudolph, Amr Rizk

Abstract: Learned point cloud compression methods have achieved rate-distortion performance, which is comparable to or higher than conventional approaches. However, this often comes at the cost of high hardware requirements and thus low throughput during encoding and decoding. In this paper, we present an adaptive bitrate point cloud streaming system utilizing learned compression. While other learned compression techniques require to split geometry and attributes, resulting in high encoding latency, we deploy a unified model to handle both modalities together, which drastically reduces the coding complexity. We explore the capabilities of the learned encoder to derive multiple quality representations with only re-running a fraction of the encoding steps, making it a suitable fit for adaptive bitrate streaming. Furthermore, we ablate the encoding latency of each component in the encoder and decoder stack, identifying bottlenecks in the process.

External IDs:dblp:conf/mmsys/0006R25