用视觉与对称先验，从单侧图像重建带真实尺度的完整车辆3D模型。
Leveraging Visual and Geometric Priors for Metric-scale and Complete Vehicle Gaussian Reconstruction from Limited Views
- 结合视觉基础模型与对称性先验，初始化并优化3D高斯表示。
- 在公开数据集上，完整度和几何精度均显著优于现有方法。
- 适合需要高保真车辆资产的自动驾驶仿真与长尾场景生成。
高保真车辆资产对可控交通场景生成至关重要，尤其适用于合成稀有且安全关键的长尾场景。然而，从真实环境车载图像中重建可复用的车辆表示仍面临两大挑战：其一，图像到3D生成方法通常无法保证可靠的真实尺度；其二，车载摄像头通常仅观测车辆一侧，导致传统多视图重建在未观测区域不完整。为此，我们提出一种前馈式车辆资产重建方法，利用两种互补先验，基于稀疏单侧观测重建车辆的3D高斯表示。为实现真实尺度重建，首先使用视觉基础模型作为视觉先验初始化高斯，再通过可学习编码器-解码器模块估计高斯属性。提出一种感知对称性的克隆策略，在高斯空间直接补全未观测侧，利用车辆的双边结构作为几何先验。在公开数据集上的实验表明，所提方法在车辆资产完整性和几何准确性方面均显著优于现有方法。
原文摘要 · Abstract (English)
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.
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