# CATNIPS: Collision Avoidance Through Neural Implicit Probabilistic Scenes

arXiv: https://arxiv.org/abs/2302.12931 · HTML: https://mlsanity.com/paper/2302.12931/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2023.

## Built on by (most cited first, 20 of 20)

- [Point-SLAM: Dense Neural Point Cloud-based SLAM](https://mlsanity.com/paper/2304.04278/) (2023)
- [Splat-MOVER: Multi-Stage, Open-Vocabulary Robotic Manipulation via Editable Gaussian Splatting](https://mlsanity.com/paper/2405.04378/) (2024)
- [What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models](https://mlsanity.com/paper/2512.03422/) (2025)
- [Neural Fields in Robotics: A Survey](https://mlsanity.com/paper/2410.20220/) (2024)
- [Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting](https://mlsanity.com/paper/2403.09875/) (2024)
- [NeRFs in Robotics: A Survey](https://mlsanity.com/paper/2405.01333/) (2024)
- [AI-Generated Images as Data Source: The Dawn of Synthetic Era](https://mlsanity.com/paper/2310.01830/) (2023)
- [Neural Radiance Fields for the Real World: A Survey](https://mlsanity.com/paper/2501.13104/) (2025)
- [GRaD-Nav: Efficiently Learning Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics](https://mlsanity.com/paper/2503.03984/) (2025)
- [These Magic Moments: Differentiable Uncertainty Quantification of Radiance Field Models](https://mlsanity.com/paper/2503.14665/) (2025)
- [NeRFuser: Large-Scale Scene Representation by NeRF Fusion](https://mlsanity.com/paper/2305.13307/) (2023)
- [I2-SLAM: Inverting Imaging Process for Robust Photorealistic Dense SLAM](https://mlsanity.com/paper/2407.11347/) (2024)
- [Distributed NeRF Learning for Collaborative Multi-Robot Perception](https://mlsanity.com/paper/2409.20289/) (2024)
- [Scan, Materialize, Simulate: A Generalizable Framework for Physically Grounded Robot Planning](https://mlsanity.com/paper/2505.14938/) (2025)
- [Geometry Meets Vision: Revisiting Pretrained Semantics in Distilled Fields](https://mlsanity.com/paper/2510.03104/) (2025)
- [VLM-DEWM: Dynamic External World Model for Verifiable and Resilient Vision-Language Planning in Manufacturing](https://mlsanity.com/paper/2602.15549/) (2026)
- [Data Augmentation for NeRFs in the Low Data Limit](https://mlsanity.com/paper/2503.02092/) (2025)
- [Embodied Spatial Intelligence: from Implicit Scene Modeling to Spatial Reasoning](https://mlsanity.com/paper/2509.00465/) (2025)
- [Neural Radiance Maps for Extraterrestrial Navigation and Path Planning](https://mlsanity.com/paper/2603.17236/) (2026)
- [SE(3) Neural Potential Fields for 6-DoF Trajectory Planning Directly from Images Without Explicit 3D Reconstruction](https://mlsanity.com/paper/2609.24864/) (2026)

## Abstract (arXiv)

We introduce a transformation of a Neural Radiance Field (NeRF) to an equivalent Poisson Point Process (PPP). This PPP transformation allows for rigorous quantification of uncertainty in NeRFs, in particular, for computing collision probabilities for a robot navigating through a NeRF environment. The PPP is a generalization of a probabilistic occupancy grid to the continuous volume and is fundamental to the volumetric ray-tracing model underlying radiance fields. Building upon this PPP representation, we present a chance-constrained trajectory optimization method for safe robot navigation in NeRFs. Our method relies on a voxel representation called the Probabilistic Unsafe Robot Region (PURR) that spatially fuses the chance constraint with the NeRF model to facilitate fast trajectory optimization. We then combine a graph-based search with a spline-based trajectory optimization to yield robot trajectories through the NeRF that are guaranteed to satisfy a user-specific collision probability. We validate our chance constrained planning method through simulations and hardware experiments, showing superior performance compared to prior works on trajectory planning in NeRF environments. Our codebase can be found at https://github.com/chengine/catnips, and videos can be found on our project page (https://chengine.github.io/catnips).