# Beyond Uncertainty: Risk-Aware Active View Acquisition for Safe Robot Navigation and 3D Scene Understanding with FisherRF

arXiv: https://arxiv.org/abs/2403.11396 · HTML: https://mlsanity.com/paper/2403.11396/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2024.

## Built on by (most cited first, 10 of 10)

- [ActiveSplat: High-Fidelity Scene Reconstruction through Active Gaussian Splatting](https://mlsanity.com/paper/2410.21955/) (2024)
- [What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models](https://mlsanity.com/paper/2512.03422/) (2025)
- [POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality](https://mlsanity.com/paper/2503.07819/) (2025)
- [Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses](https://mlsanity.com/paper/2605.02900/) (2026)
- [From NeRFs to Gaussian Splats, and Back](https://mlsanity.com/paper/2405.09717/) (2024)
- [ExploreGS: Explorable 3D Scene Reconstruction with Virtual Camera Samplings and Diffusion Priors](https://mlsanity.com/paper/2508.06014/) (2025)
- [Frequency-based View Selection in Gaussian Splatting Reconstruction](https://mlsanity.com/paper/2409.16470/) (2024)
- [Active Next-Best-View Optimization for Risk-Averse Path Planning](https://mlsanity.com/paper/2510.06481/) (2025)
- [FillGS: Filling Observation Gaps in 4D Gaussian Splatting via Viewpoint-Time Selection and Generative Refinement](https://mlsanity.com/paper/2607.29284/) (2026)
- [LiTe-GS: Oracle-Efficient Next Best View Selection for 3D Gaussian Splatting](https://mlsanity.com/paper/2609.30393/) (2026)

## Abstract (arXiv)

The active view acquisition problem has been extensively studied in the context of robot navigation using NeRF and 3D Gaussian Splatting. To enhance scene reconstruction efficiency and ensure robot safety, we propose the Risk-aware Environment Masking (RaEM) framework. RaEM leverages coherent risk measures to dynamically prioritize safety-critical regions of the unknown environment, guiding active view acquisition algorithms toward identifying the next-best-view (NBV). Integrated with FisherRF, which selects the NBV by maximizing expected information gain, our framework achieves a dual objective: improving robot safety and increasing efficiency in risk-aware 3D scene reconstruction and understanding. Extensive high-fidelity experiments validate the effectiveness of our approach, demonstrating its ability to establish a robust and safety-focused framework for active robot exploration and 3D scene understanding.