The paper introduces a new stereo VR180 dataset captured directly with Blackmagic URSA Cine Immersive cameras, comprising 1,211 samples (636 video clips and 575 stills). Each sample is provided in camera-native Blackmagic RAW, separate-eye fisheye HEVC, and half-equirectangular HEVC formats, along with factory lens calibration and conversion tools. The authors also demonstrate that native-fisheye coding requires significantly more bitrate than half-equirectangular coding for equivalent viewport quality across all tested clips.
arXiv:2508. 04928v5 Announce Type: replace-cross Abstract: We propose a method to extend foundational monocular depth estimators (FMDEs), trained on perspective images, to fisheye images.
arXiv:2608.23140v1 Announce Type: new Abstract: Achieving 360{\deg} coverage is critical for the visual perception systems of autonomous vehicles. Fisheye cameras offer a cost-effective solution by e...
arXiv:2511. 20853v4 Announce Type: replace-cross Abstract: Training and evaluation of state-of-the-art computer vision algorithms for reliable shallow depth of field (DoF) rendering and defocus deblurring remain constrained by a persistent lack of large-scale, full-frame, high fidelity, real-image datasets.
The paper introduces Distortion Extenders (DEX), learnable parameters that adapt vision foundation models to fisheye cameras by modeling distortion coefficients and correcting distributional shifts between fisheye and perspective images. DEX is applied to monocular depth estimation and open‑vocabulary segmentation across convolutional and Transformer architectures, consistently outperforming baselines on indoor and outdoor fisheye datasets. Additionally, DEX activations can be decoded to obtain distortion coefficients, aiding camera calibration.
The paper introduces a 2D Gaussian Splatting pipeline that renders a dominant-eye RGB image and depth proxy, then reprojects and selectively patches the affiliated eye to reduce redundant work. By reusing alpha-blending weights and generating adaptive regions of interest, the method cuts sequential binocular rendering time by 15.5% to 28.8% and GPU memory by 6% to 11% on several datasets, with minimal quality loss. It demonstrates a practical efficiency‑quality trade‑off for static‑scene stereo rendering and suggests further evaluation on dynamic scenes and VR hardware.
FUSEye is a lightweight training framework that adapts a frozen COCO‑pretrained YOLO26‑x detector for fisheye images by adding only about 227k parameters. It introduces three modules: GridViews to enlarge compressed boundary regions, Z‑Adapters to correct distortion‑induced feature misalignment, and AgreeFusion to fuse detections across overlapping views. On the WoodScape fisheye benchmark, FUSEye boosts YOLO26‑x mAP50 from 0.148 to 0.266 while requiring only 25% of the labeled data to achieve 0.2597 mAP50, and it also improves YOLOv8‑11 detectors.
arXiv:2605.12938v2 Announce Type: replace-cross Abstract: Video world models should predict future appearance in a way that remains consistent with 3D scene structure, camera motion, and lens geometr...
arXiv:2608.22965v1 Announce Type: new Abstract: Accurate extrinsic calibration between event-based and frame-based cameras remains a practical bottleneck for heterogeneous stereo systems. Existing ap...
arXiv:2608.22465v1 Announce Type: new Abstract: High-fidelity free-viewpoint video (FVV) and interactive rendering increasingly rely on explicit Gaussian representations, yet practical deployment rem...
OmniShowcase introduces a method for generating omnidirectional videos of a specific subject from a handful of multi‑view images. It combines an automated data‑synthesis pipeline that creates training videos with diverse camera trajectories driven by the subject’s geometry, and a geometry‑conditioning technique that supplies the model with surface‑normal maps reconstructed from the reference views. The authors release Showcase‑4K, a large corpus of 3D‑asset‑based videos with 12 camera motions and 88 trajectory configurations, and ShowcaseBench, a held‑out evaluation set with source meshes and camera parameters.
The study examines how different two‑dimensional projections of spherical 360‑degree video affect end‑to‑end neural compression. Seven JVET‑360Lib formats were evaluated using a scale‑space flow model on standard test sequences, with performance measured by PSNR, spherical PSNR, weighted spherical PSNR, and BD‑rate. Results show that equirectangular and padded equirectangular projections yield the best compression efficiency with the neural model, while cubemap‑based formats excel with conventional HM‑16.16 codecs, highlighting that projection choice is codec‑dependent.
arXiv:2610.34854v1 Announce Type: new Abstract: When zooming between dual cameras on smartphones, prominent discontinuities in both geometric perspective and color rendition inevitably degrade the vi...