Have We Scene It All?

Scene Graph-Aware Deep Point Cloud Compression

Nikolaos Stathoulopoulos · Christoforos Kanellakis · George Nikolakopoulos

💡 Introduction

Abstract: 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.

Code and pre-trained weights will be released before January 2026

📝 Citation

If you found this work useful, please cite the following publication:

@article{stathoulopoulos2025sgadpcc,
  author={Stathoulopoulos, Nikolaos and Kanellakis, Christoforos and Nikolakopoulos, George},
  journal={IEEE Robotics and Automation Letters}, 
  title={{Have We Scene It All? Scene Graph-Aware Deep Point Cloud Compression}}, 
  year={2025},
  pages={1-8},
  doi={10.1109/LRA.2025.3623045}
}