{"slug": "wide-view-synthesis-from-overlapping-perspective-cameras-in-autonomous-driving", "title": "Wide View Synthesis from Overlapping Perspective Cameras in Autonomous Driving", "summary": "Researchers posting arXiv paper 2611.07081v1 introduced Wide-View Gaussian Splatting (WV-GS), a feed-forward Gaussian-splatting framework that synthesizes a geometrically consistent wide view from multiple perspective cameras in autonomous driving without per-scene optimization. WV-GS adds Piecewise Perspective Rendering (PPR) to reduce peripheral stretching via local perspective projections sharing a common camera center, and Spatial Affine Photometric Modeling (SAPM) to handle camera-dependent photometric variation during training. The authors also released WideDrive, a synthetic benchmark with dense ground-truth wide views, and adapted a LiDAR-projected sparse-reference protocol to real-world datasets; dataset and code are to be publicly released.", "body_md": "arXiv:2611.07081v1 Announce Type: new \nAbstract: Multi-perspective-camera systems are widely adopted since they provide broad visual coverage for autonomous driving, motivating the synthesis of a unified wide view from spatially separated observations. In this work, we formulate wide view synthesis (WVS), which aims to synthesize a geometrically consistent wide view at a specified virtual viewpoint from multiple perspective cameras. Unlike conventional image stitching, WVS requires reasoning about 3D geometry and visibility to handle depth-dependent parallax and occlusions arising from spatially separated camera centers. While vanilla 3D Gaussian Splatting (3DGS) enables high-fidelity view synthesis, its reliance on per-scene optimization limits its applicability to driving scenes. Furthermore, WVS introduces additional challenges, *e.g.*, peripheral stretching under perspective projection and photometric inconsistencies across cameras. To address these challenges, we propose **Wide-View Gaussian Splatting (WV-GS)**, a feed-forward Gaussian-splatting framework that synthesizes wide views without per-scene optimization. Specifically, we introduce Piecewise Perspective Rendering (PPR) to mitigate peripheral stretching through local perspective projections sharing a common camera center, and Spatial Affine Photometric Modeling (SAPM) to account for camera-dependent photometric variations during training. We further introduce **WideDrive**, a synthetic benchmark providing dense ground-truth wide views and adapt a LiDAR-projected sparse-reference protocol to real-world datasets. Extensive experiments on multiple datasets demonstrate the effectiveness of WV-GS for WVS. Dataset and code will be publicly released.", "url": "https://wpnews.pro/news/wide-view-synthesis-from-overlapping-perspective-cameras-in-autonomous-driving", "canonical_source": "https://arxiv.org/abs/2611.07081", "published_at": "2026-11-06 05:00:00+00:00", "updated_at": "2026-11-06 05:23:46.311013+00:00", "lang": "en", "topics": ["autonomous-vehicles", "computer-vision", "ai-research", "machine-learning"], "entities": ["arXiv", "Wide-View Gaussian Splatting", "Piecewise Perspective Rendering", "Spatial Affine Photometric Modeling", "WideDrive", "3D Gaussian Splatting"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/wide-view-synthesis-from-overlapping-perspective-cameras-in-autonomous-driving", "markdown": "https://wpnews.pro/news/wide-view-synthesis-from-overlapping-perspective-cameras-in-autonomous-driving.md", "text": "https://wpnews.pro/news/wide-view-synthesis-from-overlapping-perspective-cameras-in-autonomous-driving.txt", "jsonld": "https://wpnews.pro/news/wide-view-synthesis-from-overlapping-perspective-cameras-in-autonomous-driving.jsonld"}}