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...
MeSS (Mesh-based Scene Synthesis) is a method that generates high‑quality, style‑consistent outdoor scenes using city mesh models as geometric priors. The pipeline first creates geometrically consistent sparse views with Cascaded Outpainting ControlNets, then densifies intermediate views via AGInpaint, and finally removes visual inconsistencies with GCAlign. Simultaneously, a 3D Gaussian Splatting scene is reconstructed from the mesh, enabling diverse rendering styles after synthesis.
GroundControl introduces a satellite‑conditioned multi‑view diffusion model for novel view synthesis that uses satellite images alongside street‑level inputs. The cross‑attention conditioning preserves pretrained diffusion priors while ensuring realistic spatial layouts, even when target views have little overlap with source views. Experiments on outdoor datasets demonstrate superior performance over both satellite‑only and street‑only baselines without relying on 3D reconstruction.
GroundControl introduces a satellite-conditioned multi‑view diffusion model for novel view synthesis that leverages both satellite imagery and street‑level inputs. The approach uses cross‑attention conditioning to preserve pretrained diffusion priors while aligning generated views with the scene layout seen from satellite images, avoiding unrealistic hallucinations. Experiments on outdoor datasets demonstrate superior performance over baselines, particularly when target views have minimal overlap with source views.
The paper introduces ESC, a framework that improves 3D-consistent novel view synthesis from a single 2D image by jointly generating a target view and its depth map. By conditioning the diffusion model on the source image, source depth, and target camera rays, ESC forces the model to reason in 3D even under extreme viewpoint changes. A per‑pixel depth consistency constraint is added to enforce spatial fidelity, resulting in superior visual accuracy and spatial alignment compared to current state‑of‑the‑art methods.
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:2610.19502v1 Announce Type: new Abstract: Spatial intelligence requires scalable 3D environments that are both photorealistic and semantically grounded, yet existing indoor datasets are dominat...
arXiv:2610.14315v2 Announce Type: cross Abstract: Reconstructing accurate camera poses and floor-plan layouts from sparse, unposed, wide-baseline RGB panoramas remains a challenging open problem. Dir...
AdaGS introduces a confidence-gated diffusion adaptation framework to improve unposed sparse-view 3D Gaussian reconstruction. The method uses an artifact-aware adapter trained on a Gaussian artifact dataset to suppress floating artifacts before diffusion restoration, and employs bidirectional confidence fusion to accept generated regions only when supported by cross-view geometry. Additionally, a Gaussian stabilization strategy reduces weakly inconsistent Gaussians that could cause hallucinations, leading to better rendering fidelity and pose accuracy on three outdoor datasets.
arXiv:2604.13183v4 Announce Type: replace Abstract: Generalizable cross-view geo-localization aims to match the same location across views in unseen regions and conditions without GPS supervision. It...
The paper proposes a new sequence-to-sequence formulation for multi-view stereo (MVS) that jointly predicts 3D geometry for all input views using a global transformer architecture. It introduces ray‑map embeddings to inject camera parameters into image tokens and a unified global cost volume to capture 3D structure across all views. Experiments on public benchmarks demonstrate state‑of‑the‑art performance, outperforming both traditional MVS and feed‑forward reconstruction baselines.
arXiv:2610.32800v1 Announce Type: new Abstract: Real-world 3D reconstruction pipelines often rely on multi-view images degraded by lossy compression. While compression artifacts have been extensively...