Abstract
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.
Keywords
Publication details
- Journal
- Not available
- Open access
- Green open access
Cite this article
APA 7
Khorrambakht, R., Ortiz-Haro, J., Weiss, S., & Righetti, L. (2026). CollisionSplatting: Collision-Aware Motion Planning in 3DGS Scenes with Image-Conditioned Objectives and Adjustable Conservatism. https://omanscience.com/en/articles/collisionsplatting-collision-aware-motion-planning-in-3dgs-scenes-with-image-conditioned-objectives-and-adjustable-conservatism
MLA 9
Khorrambakht, R., et al. "CollisionSplatting: Collision-Aware Motion Planning in 3DGS Scenes with Image-Conditioned Objectives and Adjustable Conservatism." https://omanscience.com/en/articles/collisionsplatting-collision-aware-motion-planning-in-3dgs-scenes-with-image-conditioned-objectives-and-adjustable-conservatism.
Chicago (author–date)
Khorrambakht, R., Joaquim Ortiz-Haro, Stephan Weiss, and Ludovic Righetti. 2026. "CollisionSplatting: Collision-Aware Motion Planning in 3DGS Scenes with Image-Conditioned Objectives and Adjustable Conservatism." https://omanscience.com/en/articles/collisionsplatting-collision-aware-motion-planning-in-3dgs-scenes-with-image-conditioned-objectives-and-adjustable-conservatism.
Harvard
Khorrambakht, R., Ortiz-Haro, J., Weiss, S. and Righetti, L. (2026) 'CollisionSplatting: Collision-Aware Motion Planning in 3DGS Scenes with Image-Conditioned Objectives and Adjustable Conservatism', Available at: https://omanscience.com/en/articles/collisionsplatting-collision-aware-motion-planning-in-3dgs-scenes-with-image-conditioned-objectives-and-adjustable-conservatism.
Vancouver
Khorrambakht R, Ortiz-Haro J, Weiss S, Righetti L. CollisionSplatting: Collision-Aware Motion Planning in 3DGS Scenes with Image-Conditioned Objectives and Adjustable Conservatism. https://omanscience.com/en/articles/collisionsplatting-collision-aware-motion-planning-in-3dgs-scenes-with-image-conditioned-objectives-and-adjustable-conservatism
IEEE
R. Khorrambakht, J. Ortiz-Haro, S. Weiss, and L. Righetti, "CollisionSplatting: Collision-Aware Motion Planning in 3DGS Scenes with Image-Conditioned Objectives and Adjustable Conservatism," https://omanscience.com/en/articles/collisionsplatting-collision-aware-motion-planning-in-3dgs-scenes-with-image-conditioned-objectives-and-adjustable-conservatism.