Have We Scene It All? Scene Graph-Aware Deep Point Cloud Compression
This addresses bandwidth constraints in multi-agent robotic systems, enabling advanced perception under intermittent connectivity, though it is an incremental improvement over existing compression methods.
The paper tackles efficient transmission of 3D point cloud data for robotic systems by proposing a deep compression framework based on semantic scene graphs, achieving state-of-the-art compression rates with up to 98% data size reduction while preserving structural and semantic fidelity.
Efficient transmission of 3D point cloud data is critical for advanced perception in centralized and decentralized multi-agent robotic systems, especially nowadays with the growing reliance on edge and cloud-based processing. However, the large and complex nature of point clouds creates challenges under bandwidth constraints and intermittent connectivity, often degrading system performance. We propose a deep compression framework based on semantic scene graphs. The method decomposes point clouds into semantically coherent patches and encodes them into compact latent representations with semantic-aware encoders conditioned by Feature-wise Linear Modulation (FiLM). A folding-based decoder, guided by latent features and graph node attributes, enables structurally accurate reconstruction. Experiments on the SemanticKITTI and nuScenes datasets show that the framework achieves state-of-the-art compression rates, reducing data size by up to 98% while preserving both structural and semantic fidelity. In addition, it supports downstream applications such as multi-robot pose graph optimization and map merging, achieving trajectory accuracy and map alignment comparable to those obtained with raw LiDAR scans.