← 研踪 PaperTrailCVF · CVPR 2026:cvf:8a8ae2186700ecdecd0caf957741930f · 记录 #1
GSV2X: Geometry-Aware Uncertainty Modeling and Orthogonal Fusion for Robust Roadside Perception
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 01:19
摘要
Reliable 3D perception from multi-view roadside sensors hinges on the robust fusion of camera and LiDAR data, a task complicated by geometric misalignments and sensor calibration errors. This paper presents GSV2X, a fusion framework that tackles these challenges through two core contributions. First, to achieve robustness against spatial uncertainty, we lift 2D image features into a unified Bird's-Eye-View (BEV) space by representing them as 3D Gaussian distributions. By incorporating learnable perturbations guided by camera geometry, our model explicitly accounts for potential calibration inaccuracies. Second, to maximize the synergy between modalities, we propose a new orthogonal fusion module. This module employs constrained attention to enforce orthogonality between camera and LiDAR features, effectively disentangling redundant information and promoting the learning of complementary representations. Extensive experiments on the challenging RCooper dataset demonstrate that GSV2X sets a new state-of-the-art in multi-view roadside perception and exhibits remarkable robustness in complex, real-world scenarios.
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记录 #1 · 2026 · 最新观测版本
GSV2X: Geometry-Aware Uncertainty Modeling and Orthogonal Fusion for Robust Roadside Perception
Reliable 3D perception from multi-view roadside sensors hinges on the robust fusion of camera and LiDAR data, a task complicated by geometric misalignments and sensor calibration errors. This paper presents GSV2X, a fusion framework that tackles these challenges through two core contributions. First, to achieve robustness against spatial uncertainty, we lift 2D image features into a unified Bird's-Eye-View (BEV) space by representing them as 3D Gaussian distributions. By incorporating learnable perturbations guided by camera geometry, our model explicitly accounts for potential calibration inaccuracies. Second, to maximize the synergy between modalities, we propose a new orthogonal fusion module. This module employs constrained attention to enforce orthogonality between camera and LiDAR features, effectively disentangling redundant information and promoting the learning of complementary representations. Extensive experiments on the challenging RCooper dataset demonstrate that GSV2X sets a new state-of-the-art in multi-view roadside perception and exhibits remarkable robustness in complex, real-world scenarios.
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