Hydra++: 物体形状推定を備えたリアルタイム階層型3Dシーングラフ構築
Hydra++: Real-Time Hierarchical 3D Scene Graph Construction With Object-Level Shape Estimation
3Dシーングラフに学習ベースの物体形状推定を統合し、部分点群やCADテンプレートに頼らずインスタンス固有の形状を推定するシステムを提案。オンライン構築と屋外大規模対応のためのハイブリッドLiDAR-カメラ構成も評価した。
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著者: Hyungtae Lim, Nathan Hughes, Xihang Yu, Ruihan Xu, Yun Chang, Jingnan Shi, Rajat Talak, Luca Carlone
分類: cs.CV, cs.RO
原文アブストラクト
3D scene graphs provide a hierarchical abstraction of environments by encoding spatial entities, such as objects and places, and their relationships. However, existing scene graph systems model object geometry coarsely, relying on partial point clouds or class-level CAD templates, which limits instance-specific shape detail. This paper presents Hydra++, a system-level investigation into how learning-based object shape estimators can be integrated into a hierarchical 3D scene graph pipeline. Hydra++ incorporates category-agnostic shape estimation and a reprojection-mask consistency check to reject degenerate predictions from partial observations or imprecise segmentation. In its default CRISP-based configuration, Hydra++ performs online scene graph construction; slower estimators such as SAM3D are evaluated as modular alternatives to demonstrate generalization-latency trade-offs. Furthermore, to address the challenges of sparse and noisy depth measurements in outdoor environments, Hydra++ supports a hybrid LiDAR-camera configuration for large-scale operation, improving scene-level reconstruction quality. Experiments in both simulation and real-world outdoor campus scenarios demonstrate that Hydra++ improves object- and scene-level reconstruction quality. Project page is available at https://hydra-plusplus.github.io/.
