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RefinedFields: Radiance Fields Refinement for Planar Scene Representations

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arxiv 2312.00639 v4 pith:A6L2GMSI submitted 2023-12-01 cs.CV cs.LG

RefinedFields: Radiance Fields Refinement for Planar Scene Representations

classification cs.CV cs.LG
keywords planarrefinedfieldsrepresentationsscenescenesk-planesmethodrecently
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Planar scene representations have recently witnessed increased interests for modeling scenes from images, as their lightweight planar structure enables compatibility with image-based models. Notably, K-Planes have gained particular attention as they extend planar scene representations to support in-the-wild scenes, in addition to object-level scenes. However, their visual quality has recently lagged behind that of state-of-the-art techniques. To reduce this gap, we propose RefinedFields, a method that leverages pre-trained networks to refine K-Planes scene representations via optimization guidance using an alternating training procedure. We carry out extensive experiments and verify the merit of our method on synthetic data and real tourism photo collections. RefinedFields enhances rendered scenes with richer details and improves upon its base representation on the task of novel view synthesis. Our project page can be found at https://refinedfields.github.io .

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Cited by 3 Pith papers

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

  1. MU-GeNeRF: Multi-view Uncertainty-guided Generalizable Neural Radiance Fields for Distractor-aware Scene

    cs.CV 2026-04 unverdicted novelty 7.0

    MU-GeNeRF combines source-view and target-view uncertainties via a heteroscedastic loss to enable distractor-aware generalizable NeRF reconstruction that matches scene-specific methods.

  2. HarmoGS: Robust 3D Gaussian Splatting in the Wild via Conflict-Aware Gradient Harmonization

    cs.CV 2026-05 unverdicted novelty 6.0

    HarmoGS improves in-the-wild 3D Gaussian Splatting by using semantic consistency-guided masking and dual-view conflict-aware gradient harmonization to reduce artifacts from transient distractors and cross-view inconsi...

  3. HarmoGS: Robust 3D Gaussian Splatting in the Wild via Conflict-Aware Gradient Harmonization

    cs.CV 2026-05 unverdicted novelty 5.0

    HarmoGS adds semantic consistency-guided masking and dual-view orthogonal gradient harmonization to 3D Gaussian Splatting to reduce artifacts from distractors and cross-view illumination inconsistencies.