The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
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.
Multi-view diffusion models have shown strong performance in scenes with strong geometric priors and sparse semantics, such as indoor rooms or simple outdoor environments (e.g., fields, courtyards). H...
arXiv:2609.09890v1 Announce Type: new Abstract: Multi-view diffusion models have shown strong performance in scenes with strong geometric priors and sparse semantics, such as indoor rooms or simple o...
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.
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.
Scaling 3D Gaussian Splatting (3DGS) to large outdoor scenes is costly in both data acquisition and computation. Adopting panoramic images with equirectangular projection (ERP) can reduce capture effort via their full $360^{\circ}$ field of view, yet the resulting omnipresent visibility invalidates existing partitioning strategies that rely on local camera frustums, causing block-wise optimization to degenerate into global training.