用高斯点云实现零样本物理属性预测，提升机器人抓取能力
PUGS: Zero-shot Physical Understanding with Gaussian Splatting
- 基于高斯点云重建物体并设计区域感知特征对比损失
- 在ABO-500数据集上质量预测达新最佳性能
- 无需微调即可用于真实抓取任务，适合机器人感知研究
当前机器人系统已能较好理解物体类别与姿态，但在野外环境下理解质量、摩擦力、硬度等物理属性仍具挑战。本文提出一种新方法，利用高斯点云表示重建3D物体，并实现零样本物理属性预测。重建阶段引入几何感知正则化损失以提升形状质量，以及区域感知特征对比损失以增强局部区域一致性。推理阶段设计基于特征的属性传播模块与适配高斯表示的体积分层模块。所提框架名为零样本物理理解的高斯点云方法（PUGS）。PUGS在标准的ABO-500质量预测基准上取得新最优结果。通过大量定量消融实验与定性可视化，验证了设计机制的有效性。实验表明该方法可有效支持复杂真实抓取任务。代码、数据与模型已公开于https://github.com/EverNorif/PUGS。
原文摘要 · Abstract (English)
Current robotic systems can understand the categories and poses of objects well. But understanding physical properties like mass, friction, and hardness, in the wild, remains challenging. We propose a new method that reconstructs 3D objects using the Gaussian splatting representation and predicts various physical properties in a zero-shot manner. We propose two techniques during the reconstruction phase: a geometry-aware regularization loss function to improve the shape quality and a region-aware feature contrastive loss function to promote region affinity. Two other new techniques are designed during inference: a feature-based property propagation module and a volume integration module tailored for the Gaussian representation. Our framework is named as zero-shot physical understanding with Gaussian splatting, or PUGS. PUGS achieves new state-of-the-art results on the standard benchmark of ABO-500 mass prediction. We provide extensive quantitative ablations and qualitative visualization to demonstrate the mechanism of our designs. We show the proposed methodology can help address challenging real-world grasping tasks. Our codes, data, and models are available at https://github.com/EverNorif/PUGS
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