シーングラフを活用した深層点群圧縮：全シーンを見据えて
Have We Scene It All? Scene Graph-Aware Deep Point Cloud Compression
意味的なシーングラフに基づいて点群をパッチ分割し、FiLM条件付きエンコーダと折り畳みデコーダで最大98%の圧縮を実現しつつ、構造と意味の忠実性を保つ手法を提案。
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著者: Nikolaos Stathoulopoulos, Christoforos Kanellakis, George Nikolakopoulos
分類: cs.CV, cs.RO
原文アブストラクト
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.
