发表机构
New York University; University of Klagenfurt; Artificial and Natural Intelligence Toulouse Institute (ANITI)（纽约大学; 克拉根福大学; 图卢兹人工智能与自然智能研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出CollisionSplatting，一种GPU加速、可调保守性的概率距离度量，直接作用于3DGS场景，结合图像条件奖励实现联合几何与视觉规划，在MPPI和RRT规划器中达到同等或更优碰撞分类性能，同时提高吞吐量并降低VRAM使用。
AI 中文摘要
将密集的视觉信息整合到运动规划中仍然具有挑战性，因为几何规划器依赖于抽象的场景表示，丢弃了视觉丰富性，而学习的视觉模型往往缺乏几何可解释性和计算效率。本文介绍了CollisionSplatting，一种简单、模块化、GPU加速、受概率启发的距离度量，具有可调的保守性，可直接作用于标准的3D高斯泼溅（3DGS）场景。当与学习的图像条件奖励函数结合时，该度量通过统一碰撞感知成本与图像空间目标，实现了几何与视觉的联合规划。我们将该度量集成到GPU加速的模型预测路径积分（MPPI）和快速探索随机树（RRT）规划器中，并展示了与代表性基线相比，碰撞分类性能相当或更优，同时实现了显著更高的碰撞检查吞吐量和显著更低的VRAM使用。最后，我们展示了该度量在真实世界视觉引导导航和操作任务中的有效性，突出了3DGS作为丰富感知与实时运动规划之间实用桥梁的作用。
英文摘要
Incorporating dense visual information into motion planning remains challenging, as geometric planners rely on abstracted scene representations that discard visual richness, while learned visual models often lack geometric interpretability and computational efficiency. This paper introduces CollisionSplatting, a simple, modular, GPU-accelerated, probability-inspired distance metric with tunable conservatism that operates directly on standard 3D Gaussian Splatting (3DGS) scenes. When combined with learned image-conditioned reward functions, this metric enables joint geometric and visual planning by unifying collision-aware costs with image-space objectives. We integrate the metric into GPU-accelerated Model Predictive Path Integral (MPPI) and Rapidly-Exploring Random Tree (RRT) planners, and show on-par or better collision-classification performance compared to representative baselines while achieving substantially higher collision-checking throughput and significantly lower VRAM usage. Finally, we demonstrate the effectiveness of our metric in real-world vision-guided navigation and manipulation tasks, highlighting 3DGS as a practical bridge between rich perception and real-time motion planning.
