Pith. sign in

REVIEW 4 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2311.17874 v2 pith:CF656AY2 submitted 2023-11-29 cs.CV

Wen Jiang , Boshu Lei , Kostas Daniilidis This is my paper · ORCID
classification cs.CV
keywords fieldsinformationradianceuncertaintyactivemodelquantificationselection
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. G$^2$ARD-GS: Geometry-Guided Anchor-Regularized Gaussian Splatting Distillation

    cs.CV 2026-08 conditional novelty 6.0 of 10

    A progressive multi-round distillation scheme compresses LiDAR-assisted 3D Gaussian maps 5 to 30 times while preserving rendering quality and frozen-geometry reuse.

  2. GO-PRE: Goal-Oriented Next-Best-View Selection via Predictive Rendering Entropy for Active 3D Reconstruction

    cs.CV 2026-07 conditional novelty 6.0 of 10

    GO-PRE proposes a next-best-view selection score that minimizes an upper bound on predictive rendering entropy over a user-specified target view manifold for 3D Gaussian Splatting.

  3. VISTA: Open-Vocabulary, Task-Relevant Robot Exploration with Online Semantic Gaussian Splatting

    cs.RO 2025-07 conditional novelty 6.0 of 10

    VISTA couples a view-diversity information metric with CLIP semantics in a receding-horizon planner to improve open-vocabulary object search during online Gaussian Splatting mapping on robots.

  4. Gaussian Process-Based Active Exploration Strategies in Vision and Touch

    cs.RO 2025-07 conditional novelty 4.0 of 10

    A robot arm uses Gaussian Process Distance Fields to fuse RGBD vision and tactile contacts, actively choosing next views and touch points to reduce shape uncertainty, while material classification remains near chance.

Pith tools