DriveExplorer: 基于图像的解耦4D重建与渐进修复用于驾驶视角外推
DriveExplorer: Images-Only Decoupled 4D Reconstruction with Progressive Restoration for Driving View Extrapolation
AI总结：
DriveExplorer通过图像解耦和渐进修复实现驾驶视角外推，利用4D高斯框架和扩散模型提升重建质量。
摘要：
本文提出了一种有效的解决方案，用于自动驾驶场景中的视角外推。近期的方法专注于从给定视角生成移位的新型视角图像，使用扩散模型。然而，这些方法严重依赖于先验信息，如激光雷达点云、3D包围盒和车道标注，这需要昂贵的传感器或大量人工标注，限制了在现实世界中的应用。在本工作中，仅使用图像和可选的相机姿态，我们首先估计一个全局静态点云和每帧动态点云，并将它们融合为统一的表示。然后，我们采用可变形4D高斯框架来重建场景。最初训练的4D高斯模型会渲染退化和伪图像以训练视频扩散模型。随后，渐进式移位的高斯渲染结果会通过扩散模型逐步优化，并将优化后的结果重新纳入4DGS的训练数据中。这一过程持续进行，直到外推达到目标视角。与基线方法相比，我们的方法在新型外推视角上生成的图像质量更高。
英文摘要：
This paper presents an effective solution for view extrapolation in autonomous driving scenarios. Recent approaches focus on generating shifted novel view images from given viewpoints using diffusion models. However, these methods heavily rely on priors such as LiDAR point clouds, 3D bounding boxes, and lane annotations, which demand expensive sensors or labor-intensive labeling, limiting applicability in real-world deployment. In this work, with only images and optional camera poses, we first estimate a global static point cloud and per-frame dynamic point clouds, fusing them into a unified representation. We then employ a deformable 4D Gaussian framework to reconstruct the scene. The initially trained 4D Gaussian model renders degraded and pseudo-images to train a video diffusion model. Subsequently, progressively shifted Gaussian renderings are iteratively refined by the diffusion model,and the enhanced results are incorporated back as training data for 4DGS. This process continues until extrapolation reaches the target viewpoints. Compared with baselines, our method produces higher-quality images at novel extrapolated viewpoints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
