Problem
Research questions and friction points this paper is trying to address.
metric-scale reconstruction
incomplete multi-view reconstruction
vehicle 3D model
Leveraging Visual and Geometric Priors for Metric-scale and Complete Vehicle Gaussian Reconstruction from Limited Views
🤖 AI Summary
为解决从有限视角重建车辆3D模型的度量尺度和完整性问题，提出一种结合视觉和几何先验的方法，通过单侧稀疏观测重建车辆的高斯表示。
📝 Abstract
High-fidelity vehicle assets are essential for controllable traffic scene generation, particularly for synthesizing rare and safety-critical long-tail scenarios. However, reconstructing a reusable vehicle representation from in-the-wild onboard images remains challenging for two reasons. First, image-to-3D generation methods generally produce models without reliable metric scale. Second, onboard cameras usually observe only one side of a target vehicle, making conventional multi-view reconstruction incomplete on unobserved regions. To solve these problems, we propose a feed-forward vehicle asset reconstruction method, which leverages two complementary priors to reconstruct 3D Gaussian representations for vehicles using sparse one-sided observations. To achieve metric-scale reconstruction, a visual foundation model is first utilized to serve as a visual prior for Gaussian initialization. The Gaussian attributes are then estimated by a learnable encoder-decoder module. A symmetry-aware cloning strategy is presented to complete the unobserved side directly in Gaussian space, which exploits the bilateral structure of vehicles as a geometric prior. Experiments on the public dataset demonstrate that the proposed method significantly outperforms existing approaches in both vehicle asset completeness and geometric accuracy.
Innovation
Methods, ideas, or system contributions that make the work stand out.
visual prior
geometric prior
Gaussian representation
symmetry-aware cloning
metric-scale reconstruction
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