发表机构
Laboratory for Information & Decision Systems, Massachusetts Institute of Technology（信息与决策系统实验室，麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何将基于学习的对象形状估计器集成到分层3D场景图管道中，提出Hydra++系统。它结合形状估计和一致性检查，默认配置可在线构建场景图，支持混合配置应对室外挑战，实验证明其提高了对象和场景级重建质量。
AI 中文摘要
3D场景图通过编码空间实体及其关系提供环境的分层抽象。然而，现有场景图系统对对象几何形状的建模较为粗糙，限制了实例特定的形状细节。本文提出Hydra++，研究如何将基于学习的对象形状估计器集成到分层3D场景图管道中。它结合了类别无关的形状估计和重投影掩码一致性检查，以拒绝部分观测或不精确分割产生的退化预测。在默认基于CRISP的配置中，Hydra++执行在线场景图构建；评估了如SAM3D等较慢的估计器作为模块化替代方案，以展示泛化-延迟权衡。此外，为应对室外环境中稀疏和噪声深度测量的挑战，Hydra++支持混合LiDAR-相机配置以进行大规模操作，提高场景级重建质量。模拟和真实世界室外校园场景实验表明Hydra++提高了对象和场景级重建质量。
英文摘要
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/.
Comments8 pages, 12 figures, accepted in Proc. IEEE/RSJ IROS
