Squaring-the-Sphere introduces CubeAtlas, a new panoramic representation that unifies all cubemap faces into a single canvas, preserving perspective geometry while enabling seamless panorama assembly. The framework leverages a pretrained diffusion transformer with a layout‑aware sparse attention mask to reduce irrelevant token interactions, achieving higher quality than equirectangular projection with lower computational cost. Additionally, a training‑free super‑resolution and fusion scheme enhances fine details before converting to a final ERP panorama, resulting in improved geometric fidelity, cross‑view consistency, and high‑resolution visual quality across benchmarks.
arXiv:2608.23238v1 Announce Type: new Abstract: We present Mover360, a controllable object manipulation framework for 360{\deg} images. Unlike perspective images, 360{\deg} images in equirectangular...
GeoPano is a feed‑forward framework that reconstructs geometrically accurate 3D point clouds from a single panoramic image. It introduces a panoramic‑aware attention aggregator with a novel Pano‑Attention mechanism to exploit spherical geometry and reduce cross‑view inconsistencies. A hybrid supervision strategy enforces both fine‑grained local detail in perspective views and global structural coherence in spherical space, leading to superior reconstruction accuracy, consistency, and visual fidelity compared to prior state‑of‑the‑art methods.
arXiv:2608.20691v1 Announce Type: new Abstract: Panoramic image generation is increasingly important for immersive applications such as virtual reality, augmented reality, and 3D content creation. Un...
arXiv:2607. 00832v1 Announce Type: cross Abstract: A single panorama captures the full visual sphere from one camera center, yet confines users to looking around in place without enabling true scene exploration.
arXiv:2610.28033v1 Announce Type: new Abstract: Panoptic scene graph generation (PSGG) represents scene entities with segmentation masks and directed relations. Equirectangular projection (ERP) panor...
The paper introduces a dual‑projection framework for rectifying 360° panoramic images, combining a convolutional neural network that processes equirectangular projections with a vision transformer that handles cubemap projections. Cross‑projection feature transformation and multi‑level fusion allow the two representations to interact, supporting collaborative inclination estimation and upright panorama generation. Experiments on SUN360 and M3D demonstrate significant accuracy gains, achieving 65.9% and 85.2% within a 1° error threshold and low Fréchet Inception Distance scores.
TDFNet introduces a Tri-projection Deformable Fusion Network that uses equirectangular, cube map, and tangent projections to mitigate geometric distortions in panoramic salient object detection. It incorporates a cross-projection deformable attention module for geometry-aware sampling and a latitude-guided fusion module that balances ERP and CMP features using spherical latitude priors. The network’s three-branch encoding preserves global continuity, local detail, and boundary precision, improving detection performance over existing projection-based methods.
PIS3R is an image stitching method designed to handle very large parallax by leveraging deep 3D reconstruction. It uses a visual geometry grounded transformer to estimate camera parameters and reconstruct a dense 3D point cloud from two input images, then reprojects this cloud onto a reference view to create an initial stitched image. A point-conditioned image diffusion module refines the result, reducing artifacts such as holes or noise.
LF-MultiDiffusion is a training‑free method for generating spherical panoramas that extends MultiDiffusion by adding linear projections between target and reference image spaces. It reformulates latent aggregation as a regularized least‑squares problem and solves it with a Krylov‑based iterative solver during denoising, enabling denser and more natural mappings. The approach reduces the number of generator evaluations, improves inference speed by 15.36×, and yields better visual quality, text alignment, and panoramic consistency compared to the strongest training‑free baseline.
arXiv:2610.35522v1 Announce Type: cross Abstract: Recovering an object's 3D structure and camera motion from a few uncalibrated views is a fundamental problem in computer vision. Classical structure...
arXiv:2609.23182v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting now reconstructs renderable scenes from unposed, uncalibrated images. Yet, most models supervise only photometric co...
arXiv:2510. 26800v2 Announce Type: replace-cross Abstract: There are two prevalent ways for automatic 3D scene construction: procedural generation and 2D lifting.