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...
We study relative position encoding for multi-view vision Transformers under camera heterogeneity, including varying fields of view (FoVs) or projection models. Existing rotary relative position encod...
The paper introduces G-ray, a ray-level relative position encoding for multi-view vision Transformers that remains consistent across different camera projections. By parameterizing rotary phases with camera-local ray angles, G-ray achieves projection-invariant positional consistency and can be integrated with existing encodings without extra learned parameters. Experiments on 3D reconstruction and novel-view synthesis benchmarks show that G-ray improves performance, notably reducing mean pointmap relative error by 45.8% over MapAnything.
arXiv:2605.10360v3 Announce Type: replace Abstract: While novel view synthesis (NVS) for dynamic scenes has seen significant progress, reconstructing temporally consistent geometric surfaces remains...
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:2609.36374v1 Announce Type: new Abstract: Feed-forward 3D reconstruction models have achieved impressive performance by scaling model and dataset size, but their cost excludes most research gro...
arXiv:2610.19847v1 Announce Type: new Abstract: We address inverse rendering with 3D Gaussian Splatting (3DGS) by introducing explicit visibility modeling for relighting as well as material and light...
Neural Radiance Fields (NeRF) synthesize photorealistic novel views from dense image collections, but degrade sharply when few views are available. We attribute a specific, measurable defect to the ob...
arXiv:2609.01516v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) has revolutionized 3D reconstruction and novel-view synthesis, scenarios with limited input views often lead to poor...
arXiv:2608.23206v1 Announce Type: new Abstract: We study spherical occupancy profiles-the ray-wise occupancy probability profiles P(r) = T(r) o(r) distilled from multi-view 3D Gaussian reconstruction...
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:2609. 03378v1 Announce Type: new Abstract: Depth can resolve appearance ambiguity in RGB-D salient object detection (SOD), yet sensor depth is not uniformly reliable.