[Submitted on 4 Nov 2026]
Title:Wide View Synthesis from Overlapping Perspective Cameras in Autonomous Driving
View a PDF of the paper titled Wide View Synthesis from Overlapping Perspective Cameras in Autonomous Driving, by Kaixuan Zhang and 4 other authors
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Abstract: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.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2611.07081 [cs.CV]
(or arXiv:2611.07081v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2611.07081
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kaixuan Zhang [view email] [v1] Wed, 4 Nov 2026 19:36:54 UTC (7,027 KB)
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