arXiv Computer Vision
Neighbor-Aware View Synthesis for Restoring Missing Views in Light-Field Camera Arrays
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Aug 24
Neighbor-Aware View Synthesis for Restoring Missing Views in Light-Field Camera Arrays
In light-field (LF) imaging systems, dense spatial sampling from a camera array enables powerful post-capture capabilities such as refocusing and depth estimation. However, real-world LF capture is of...
arXiv Computer Vision
Sep 15
Filling the Unseen: Scene Extrapolation via 3D Gaussian Splatting
arXiv:2609.13262v1 Announce Type: new Abstract: 3D Gaussian Splatting achieves photorealistic reconstruction within training view distribution, yet it degrades on out-of-distribution novel views, exh...
arXiv Computer Vision
Sep 3
RoGe: Novel View Synthesis via End-to-End Implicit Reconstruction and Generation
RoGe is a new end‑to‑end framework for novel view synthesis that jointly learns an implicit 3D scene representation and a video diffusion model. It eliminates the need for explicit 3D intermediates by querying the implicit scene with camera rays to produce geometric features that condition the diffusion model. Experiments on DL3DV show that RoGe surpasses reconstruction‑based, generation‑based, and hybrid baselines in image quality and temporal consistency, and ablations confirm the benefits of ray‑queried features and joint training.
arXiv Computer Vision
Aug 24
Sparse Light Field Sampling Improves Casual 3D and 4D Reconstruction
arXiv:2608.20602v1 Announce Type: cross Abstract: Many consumer smartphones, stereo cameras, and light field cameras record multiple synchronized viewpoints in a single exposure event. However, novel...
arXiv AI
Jul 21
Depth Estimators Are Implicit Neural Fields for 3D Scene Geometry Inpainting and Reconstruction
arXiv:2607. 16286v1 Announce Type: cross Abstract: The 3D geometry of real-world scene data is often incomplete.
arXiv Computer Vision
Oct 5
Feedforward Novel View Synthesis for Heterogeneous Cameras
The paper introduces a feed‑forward novel view synthesis method that works across heterogeneous cameras, such as perspective, fisheye, and panoramic types. It addresses tokenization ambiguity by combining token‑center relative Camera Positional Encodings with local raymaps and a projection‑aware 2D RoPE that aligns positional reasoning across different camera projections. Experiments on ScanNet++ show improved performance over camera‑conditioned baselines and zero‑shot generalization to panoramic views.