Pano360: Perspective to Panoramic Vision with Geometric Consistency
- URL: http://arxiv.org/abs/2603.12013v1
- Date: Thu, 12 Mar 2026 14:56:14 GMT
- Title: Pano360: Perspective to Panoramic Vision with Geometric Consistency
- Abstract summary: We adopt a novel transformer-based architecture to achieve 3D awareness and aggregate global information across all views.<n>To establish an evaluation benchmark and train our network, we constructed a large-scale dataset of real-world scenes.
- Score: 7.713672589538202
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Prior panorama stitching approaches heavily rely on pairwise feature correspondences and are unable to leverage geometric consistency across multiple views. This leads to severe distortion and misalignment, especially in challenging scenes with weak textures, large parallax, and repetitive patterns. Given that multi-view geometric correspondences can be directly constructed in 3D space, making them more accurate and globally consistent, we extend the 2D alignment task to the 3D photogrammetric space. We adopt a novel transformer-based architecture to achieve 3D awareness and aggregate global information across all views. It directly utilizes camera poses to guide image warping for global alignment in 3D space and employs a multi-feature joint optimization strategy to compute the seams. Additionally, to establish an evaluation benchmark and train our network, we constructed a large-scale dataset of real-world scenes. Extensive experiments show that our method significantly outperforms existing alternatives in alignment accuracy and perceptual quality.
Related papers
- IVGT: Implicit Visual Geometry Transformer for Neural Scene Representation [76.36174247570716]
We propose IVGT, an Implicit Visual Geometry Transformer that implicitly models continuous and coherent geometry from pose-free multi-view images.<n> IVGT learns a continuous neural scene representation in a canonical coordinate system and supports continuous spatial queries at any 3D positions.<n>It allows direct extraction of continuous and coherent surface geometry, enabling rendering of RGB images, depth maps, and surface normal maps from arbitrary viewpoints.
arXiv Detail & Related papers (2026-05-15T17:59:57Z) - Ψ-Map: Panoptic Surface Integrated Mapping Enables Real2Sim Transfer [24.777932568749446]
We propose a comprehensive framework that integrates geometric reinforcement, end-to-end panoptic learning, and efficient rendering.<n>Our system achieves superior geometric and panoptic reconstruction quality in large-scale scenes while maintaining an inference rate exceeding 40 FPS.
arXiv Detail & Related papers (2026-04-13T04:41:00Z) - PanoVGGT: Feed-Forward 3D Reconstruction from Panoramic Imagery [41.23812453847979]
PanoVGGT jointly predicts camera poses, depth maps, and 3D point clouds from one or multiple panoramas in a single forward pass.<n>In addition, we contribute PanoCity, a large-scale outdoor panoramic dataset with dense depth and 6-DoF pose annotations.
arXiv Detail & Related papers (2026-03-18T10:24:06Z) - OneWorld: Taming Scene Generation with 3D Unified Representation Autoencoder [90.8453349494245]
We present OneWorld, a framework that performs diffusion directly within a coherent 3D representation space.<n>OneWorld generates high-quality 3D scenes with superior cross-view consistency compared to state-of-the-art 2D-based methods.
arXiv Detail & Related papers (2026-03-17T03:43:37Z) - Scene Generation at Absolute Scale: Utilizing Semantic and Geometric Guidance From Text for Accurate and Interpretable 3D Indoor Scene Generation [5.189354278537469]
GuidedSceneGen produces metrically accurate, globally consistent, and semantically interpretable indoor scenes.<n>Our approach maintains an absolute world coordinate frame throughout the entire generation process.
arXiv Detail & Related papers (2026-03-14T11:46:27Z) - TALO: Pushing 3D Vision Foundation Models Towards Globally Consistent Online Reconstruction [57.46712611558817]
3D vision foundation models have shown strong generalization in reconstructing key 3D attributes from uncalibrated images through a single feed-forward pass.<n>Recent strategies align consecutive predictions by solving global transformation, yet our analysis reveals their fundamental limitations in assumption validity, local alignment scope, and robustness under noisy geometry.<n>We propose a higher-DOF and long-term alignment framework based on Thin Plate Spline, leveraging globally propagated control points to correct spatially varying inconsistencies.
arXiv Detail & Related papers (2025-12-02T02:22:20Z) - SPIDER: Spatial Image CorresponDence Estimator for Robust Calibration [28.539636818320886]
We introduce SPIDER, a universal feature matching framework that integrates a shared feature extraction backbone with two specialized network heads.<n> SPIDER significantly outperforms SoTA methods, demonstrating its strong ability as a universal image-matching method.
arXiv Detail & Related papers (2025-11-21T20:02:08Z) - LSS3D: Learnable Spatial Shifting for Consistent and High-Quality 3D Generation from Single-Image [12.683334603689502]
We propose a high-quality image-to-3D approach, named LSS3D, with learnable spatial shifting to handle the multiview inconsistencies and non-frontal input view.<n>Specifically, we assign learnable spatial shifting parameters to each view, and adjust each view towards a spatially consistent target, guided by the reconstructed mesh.<n>Our method consistently achieves leading results in both geometric and texture evaluation metrics across more flexible input viewpoints.
arXiv Detail & Related papers (2025-11-15T13:14:25Z) - WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting [51.69408870574092]
We present WorldMirror, an all-in-one, feed-forward model for versatile 3D geometric prediction tasks.<n>Our framework flexibly integrates diverse geometric priors, including camera poses, intrinsics, and depth maps.<n>WorldMirror achieves state-of-the-art performance across diverse benchmarks from camera, point map, depth, and surface normal estimation to novel view synthesis.
arXiv Detail & Related papers (2025-10-12T17:59:09Z) - IGFuse: Interactive 3D Gaussian Scene Reconstruction via Multi-Scans Fusion [15.837932667195037]
IGFuse is a novel framework that reconstructs interactive Gaussian scene by fusing observations from multiple scans.<n>Our method constructs segmentation aware Gaussian fields and enforces bi-directional photometric and semantic consistency across scans.<n>IGFuse enables high fidelity rendering and object level scene manipulation without dense observations or complex pipelines.
arXiv Detail & Related papers (2025-08-18T17:59:47Z) - CDI3D: Cross-guided Dense-view Interpolation for 3D Reconstruction [25.468907201804093]
Large Reconstruction Models (LRMs) have shown great promise in leveraging multi-view images generated by 2D diffusion models to extract 3D content.<n>However, 2D diffusion models often struggle to produce dense images with strong multi-view consistency.<n>We present CDI3D, a feed-forward framework designed for efficient, high-quality image-to-3D generation with view.
arXiv Detail & Related papers (2025-03-11T03:08:43Z) - Splatter-360: Generalizable 360$^{\circ}$ Gaussian Splatting for Wide-baseline Panoramic Images [52.48351378615057]
textitSplatter-360 is a novel end-to-end generalizable 3DGS framework to handle wide-baseline panoramic images.<n>We introduce a 3D-aware bi-projection encoder to mitigate the distortions inherent in panoramic images.<n>This enables robust 3D-aware feature representations and real-time rendering capabilities.
arXiv Detail & Related papers (2024-12-09T06:58:31Z) - Wonder3D: Single Image to 3D using Cross-Domain Diffusion [105.16622018766236]
Wonder3D is a novel method for efficiently generating high-fidelity textured meshes from single-view images.
To holistically improve the quality, consistency, and efficiency of image-to-3D tasks, we propose a cross-domain diffusion model.
arXiv Detail & Related papers (2023-10-23T15:02:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.