Hugging Face Trending Papers
DriftNeRF: Cross-Ray Density Drifting for Sparse-View Neural Radiance Fields
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arXiv Machine Learning
Oct 22
DriftNeRF: Cross-Ray Density Drifting for Sparse-View Neural Radiance Fields
DriftNeRF introduces a regularizer that addresses cross‑ray density disagreement in Neural Radiance Fields, a defect that causes poor performance when only a few views are available. By aggregating densities from nearby points on other rays into a stop‑gradient consensus target, DriftNeRF penalizes unsupported disagreement while preserving agreed boundaries. The method improves 3‑view PSNR by 0.42–0.65 dB on NeRF‑Synthetic, DTU, and LLFF datasets and remains competitive with sparse‑view Gaussian pipelines without requiring additional geometry or preprocessing.
arXiv Computer Vision
Oct 21
Geometry Regularization for Vanilla NeRF via Self-Supervised Cross-Ray Consistency
arXiv:2610.27776v1 Announce Type: new Abstract: Neural Radiance Fields (NeRF) achieve high-quality novel-view synthesis by representing a scene as a continuous volumetric radiance field. However, the...
arXiv Computer Vision
Sep 23
NaCR: Visual Localization via NeRF-aided Camera Ray Regression
NaCR: Visual Localization via NeRF-aided Camera Ray Regression proposes a unified framework that integrates Neural Radiance Fields (NeRF) with Camera Ray Regression (CRR) to improve visual localization accuracy. The method enhances the CRR baseline with three simple improvements, augments training data by synthesizing novel views from a pre‑trained NeRF, and employs a closed‑loop supervision pipeline that back‑propagates photometric rendering errors to refine predicted camera rays. A two‑stage training curriculum ensures stable convergence, and experiments on indoor and outdoor benchmarks show competitive accuracy with validated component efficacy.
arXiv Computer Vision
Sep 22
GAPS: Generative Active Pseudo-view Selection for Sparse-View 3D Gaussian Splatting
arXiv:2609.23436v1 Announce Type: new Abstract: Novel view synthesis from sparse observations is severely under-constrained. Although 3D Gaussian Splatting (3DGS) enables real-time rendering, it prod...
arXiv Computer Vision
Oct 12
Efficient Confidence-Aware Densification for 3DGS
Efficient Confidence-Aware Densification for 3DGS proposes a new densification strategy for 3D Gaussian Splatting that replaces opacity-based sampling with error-driven placement of primitives. The method keeps a fixed primitive budget and places new primitives where image error and low learned confidence are highest, relocating under-contributing primitives accordingly. Across 24 scenes, it outperforms existing fixed-budget methods—including 3DGS-MCMC, ConeGS, and EDGS—in PSNR, SSIM, and LPIPS at budgets from 100k to 2M primitives, and can also leverage prior-based initialization to match or exceed prior methods.
arXiv Computer Vision
Oct 7
DensiTok: Making Feed-Forward 3D Gaussian Splatting See More Views Than It Is Given
arXiv:2610.07958v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting (3DGS) reconstructs a scene in a single forward pass, replacing per-scene optimization with a network trained across...