发表机构
State Key Laboratory of Intelligent Green Vehicle and Mobility（智能绿色车辆与交通全国重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对车载图像重建车辆资产时缺乏度量尺度和观测不完整的问题，提出利用视觉与几何先验的前馈式3D高斯重建方法，显著提升完整性与几何精度。
AI 中文摘要
高保真车辆资产对于可控交通场景生成至关重要，尤其是在合成罕见且安全关键的长尾场景时。然而，从野外车载图像中重建可复用的车辆表示仍面临两大挑战。首先，图像到三维生成方法通常生成的模型缺乏可靠的度量尺度。其次，车载摄像头通常只能观察到目标车辆的一侧，这使得传统的多视角重建在未观测区域不完整。为解决这些问题，我们提出了一种前馈式车辆资产重建方法，利用两种互补先验，通过稀疏的单侧观测重建车辆的3D高斯表示。为实现度量尺度重建，首先利用视觉基础模型作为高斯初始化的视觉先验。随后，通过一个可学习的编码器-解码器模块估计高斯属性。我们提出了一种对称感知克隆策略，直接在高斯空间中补全未观测的一侧，该策略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精度方面均显著优于现有方法。
英文摘要
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.
Comments8 pages, 4 figures, 5 tables
