# FisherRF: Active View Selection and Uncertainty Quantification for Radiance Fields using Fisher Information

arXiv: https://arxiv.org/abs/2311.17874 · HTML: https://mlsanity.com/paper/2311.17874/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 36 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 26 of 26)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021)
- [Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields](https://mlsanity.com/paper/2304.06706/) (2023)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [Laplace Redux -- Effortless Bayesian Deep Learning](https://mlsanity.com/paper/2106.14806/) (2021)
- [A Bayesian Perspective on Generalization and Stochastic Gradient Descent](https://mlsanity.com/paper/1710.06451/) (2017)
- [ActiveNeRF: Learning where to See with Uncertainty Estimation](https://mlsanity.com/paper/2209.08546/) (2022)
- [Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields](https://mlsanity.com/paper/2309.03185/) (2023)
- [Gone Fishing: Neural Active Learning with Fisher Embeddings](https://mlsanity.com/paper/2106.09675/) (2021)
- [SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios](https://mlsanity.com/paper/2107.00717/) (2021)
- [Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representations](https://mlsanity.com/paper/2109.02123/) (2021)
- [Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification](https://mlsanity.com/paper/2203.10192/) (2022)
- [Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fields](https://mlsanity.com/paper/2209.08718/) (2022)
- [NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations](https://mlsanity.com/paper/2207.10985/) (2022)
- [Active Neural Mapping](https://mlsanity.com/paper/2308.16246/) (2023)
- [Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities](https://mlsanity.com/paper/2208.00549/) (2022)
- [Active Implicit Object Reconstruction using Uncertainty-guided Next-Best-View Optimization](https://mlsanity.com/paper/2303.16739/) (2023)
- [Uncertainty-Driven Dense Two-View Structure from Motion](https://mlsanity.com/paper/2302.00523/) (2023)

## Built on by (most cited first, 30 of 64)

- [A Survey on 3D Gaussian Splatting](https://mlsanity.com/paper/2401.03890/) (2024)
- [PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting](https://mlsanity.com/paper/2406.10219/) (2024)
- [ActiveSplat: High-Fidelity Scene Reconstruction through Active Gaussian Splatting](https://mlsanity.com/paper/2410.21955/) (2024)
- [Sort-free Gaussian Splatting via Weighted Sum Rendering](https://mlsanity.com/paper/2410.18931/) (2024)
- [4D Gaussian Splatting in the Wild with Uncertainty-Aware Regularization](https://mlsanity.com/paper/2411.08879/) (2024)
- [Eye, Robot: Learning to Look to Act with a BC-RL Perception-Action Loop](https://mlsanity.com/paper/2506.10968/) (2025)
- [GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting](https://mlsanity.com/paper/2410.23718/) (2024)
- [POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality](https://mlsanity.com/paper/2503.07819/) (2025)
- [Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting](https://mlsanity.com/paper/2403.09875/) (2024)
- [HDGS: Textured 2D Gaussian Splatting for Enhanced Scene Rendering](https://mlsanity.com/paper/2412.01823/) (2024)
- [AONeuS: A Neural Rendering Framework for Acoustic-Optical Sensor Fusion](https://mlsanity.com/paper/2402.03309/) (2024)
- [Manifold Sampling for Differentiable Uncertainty in Radiance Fields](https://mlsanity.com/paper/2409.12661/) (2024)
- [Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting](https://mlsanity.com/paper/2411.02547/) (2024)
- [FrontierNet: Learning Visual Cues to Explore](https://mlsanity.com/paper/2501.04597/) (2025)
- [GLEAM: Learning Generalizable Exploration Policy for Active Mapping in Complex 3D Indoor Scenes](https://mlsanity.com/paper/2505.20294/) (2025)
- [Sources of Uncertainty in 3D Scene Reconstruction](https://mlsanity.com/paper/2409.06407/) (2024)
- [Next Best Sense: Guiding Vision and Touch with FisherRF for 3D Gaussian Splatting](https://mlsanity.com/paper/2410.04680/) (2024)
- [MarkSplatter: Generalizable Watermarking for 3D Gaussian Splatting Model via Splatter Image Structure](https://mlsanity.com/paper/2509.00757/) (2025)
- [Multimodal LLM Guided Exploration and Active Mapping using Fisher Information](https://mlsanity.com/paper/2410.17422/) (2024)
- [These Magic Moments: Differentiable Uncertainty Quantification of Radiance Field Models](https://mlsanity.com/paper/2503.14665/) (2025)
- [PRIMU: Uncertainty Estimation for Novel Views in Gaussian Splatting from Primitive-Based Representations of Error and Coverage](https://mlsanity.com/paper/2508.02443/) (2025)
- [ExploreGS: Explorable 3D Scene Reconstruction with Virtual Camera Samplings and Diffusion Priors](https://mlsanity.com/paper/2508.06014/) (2025)
- [Uncertainty-Aware Normal-Guided Gaussian Splatting for Surface Reconstruction from Sparse Image Sequences](https://mlsanity.com/paper/2503.11172/) (2025)
- [View-Dependent Uncertainty Estimation of 3D Gaussian Splatting](https://mlsanity.com/paper/2504.07370/) (2025)
- [SpeeDe3DGS: Speedy Deformable 3D Gaussian Splatting with Temporal Pruning and Motion Grouping](https://mlsanity.com/paper/2506.07917/) (2025)
- [GSMem: 3D Gaussian Splatting as Persistent Spatial Memory for Zero-Shot Embodied Exploration and Reasoning](https://mlsanity.com/paper/2603.19137/) (2026)
- [Uncertainty-driven 3D Gaussian Splatting Active Mapping via Anisotropic Visibility Field](https://mlsanity.com/paper/2605.30342/) (2026)
- [Frequency-based View Selection in Gaussian Splatting Reconstruction](https://mlsanity.com/paper/2409.16470/) (2024)
- [Uncertainty-Aware Diffusion Guided Refinement of 3D Scenes](https://mlsanity.com/paper/2503.15742/) (2025)
- [VarSplat: Uncertainty-aware 3D Gaussian Splatting for Robust RGB-D SLAM](https://mlsanity.com/paper/2603.09673/) (2026)

## Abstract (arXiv)

This study addresses the challenging problem of active view selection and uncertainty quantification within the domain of Radiance Fields. Neural Radiance Fields (NeRF) have greatly advanced image rendering and reconstruction, but the cost of acquiring images poses the need to select the most informative viewpoints efficiently. Existing approaches depend on modifying the model architecture or hypothetical perturbation field to indirectly approximate the model uncertainty. However, selecting views from indirect approximation does not guarantee optimal information gain for the model. By leveraging Fisher Information, we directly quantify observed information on the parameters of Radiance Fields and select candidate views by maximizing the Expected Information Gain(EIG). Our method achieves state-of-the-art results on multiple tasks, including view selection, active mapping, and uncertainty quantification, demonstrating its potential to advance the field of Radiance Fields.