Pith. sign in

REVIEW 4 cited by

Active View Selector: Fast and Accurate Active View Selection with Cross Reference Image Quality Assessment

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 2506.19844 v1 pith:6YPUYCVJ submitted 2025-06-24 cs.CV

Zirui Wang , Yash Bhalgat , Ruining Li , Victor Adrian Prisacariu This is my paper
classification cs.CV
keywords viewqualityactiveselectionassessmentframeworkimagelike
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We tackle active view selection in novel view synthesis and 3D reconstruction. Existing methods like FisheRF and ActiveNeRF select the next best view by minimizing uncertainty or maximizing information gain in 3D, but they require specialized designs for different 3D representations and involve complex modelling in 3D space. Instead, we reframe this as a 2D image quality assessment (IQA) task, selecting views where current renderings have the lowest quality. Since ground-truth images for candidate views are unavailable, full-reference metrics like PSNR and SSIM are inapplicable, while no-reference metrics, such as MUSIQ and MANIQA, lack the essential multi-view context. Inspired by a recent cross-referencing quality framework CrossScore, we train a model to predict SSIM within a multi-view setup and use it to guide view selection. Our cross-reference IQA framework achieves substantial quantitative and qualitative improvements across standard benchmarks, while being agnostic to 3D representations, and runs 14-33 times faster than previous methods.

Discussion (0). Sign in 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. FillGS: Filling Observation Gaps in 4D Gaussian Splatting via Viewpoint-Time Selection and Generative Refinement

    cs.CV 2026-07 conditional novelty 6.0 of 10

    FillGS actively selects spatiotemporal virtual viewpoints using rendering sensitivity and motion-aware observation density, then fine-tunes 4D Gaussian Splatting with reliability-masked generated images, improving spa...

  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. DAV-GSWT: Diffusion-Active-View Sampling for Data-Efficient Gaussian Splatting Wang Tiles

    cs.CV 2026-02 unverdicted novelty 6.0 of 10

    DAV-GSWT uses diffusion priors and active view sampling to synthesize high-fidelity Gaussian Splatting Wang Tiles from minimal observations while preserving visual quality and tile transitions.

  4. NI-Tex: Non-isometric Image-based Garment Texture Generation

    cs.CV 2025-11 conditional novelty 6.0 of 10

    A training framework that makes image-to-garment texture transfer robust to pose and topology mismatch, using simulated garment videos, AI image editing, and uncertainty-guided multi-view baking.

Pith tools