让自动驾驶场景渲染突破固定路线限制，实现任意视角高质量重建。
FlexDrive: Toward Trajectory Flexibility in Driving Scene Reconstruction and Rendering
- 提出逆向视图扭曲技术，生成高质量监督图像以支持非路径视角重建。
- 通过在线深度自举策略，解决激光雷达深度数据稀疏不全问题，提升重建精度。
- 在Waymo数据集和仿真基准上均显著优于现有方法，适合自动驾驶视觉系统研发。
基于3D高斯泼溅的自动驾驶场景重建与渲染已取得显著进展，但多数研究仅关注预录车辆路径上的渲染质量，难以泛化至路径外视角，主要因路径外视角缺乏高质量监督信号。为此，本文提出逆向视图扭曲技术，生成紧凑且高质量的图像作为路径外视角重建的监督信号，从而实现该类视角的高质量渲染。为实现精确可靠的逆向视图扭曲，提出一种深度自举策略，在优化过程中实时获取稠密深度图，克服激光雷达深度数据稀疏不全的问题。所提方法在广泛使用的Waymo Open Dataset上实现了优异的路径内与路径外重建及渲染性能。此外，构建基于仿真的基准，获取路径外真实标签并量化评估路径外渲染效果，实验表明本方法显著优于先前方法。
原文摘要 · Abstract (English)
Driving scene reconstruction and rendering have advanced significantly using the 3D Gaussian Splatting. However, most prior research has focused on the rendering quality along a pre-recorded vehicle path and struggles to generalize to out-of-path viewpoints, which is caused by the lack of high-quality supervision in those out-of-path views. To address this issue, we introduce an Inverse View Warping technique to create compact and high-quality images as supervision for the reconstruction of the out-of-path views, enabling high-quality rendering results for those views. For accurate and robust inverse view warping, a depth bootstrap strategy is proposed to obtain on-the-fly dense depth maps during the optimization process, overcoming the sparsity and incompleteness of LiDAR depth data. Our method achieves superior in-path and out-of-path reconstruction and rendering performance on the widely used Waymo Open dataset. In addition, a simulator-based benchmark is proposed to obtain the out-of-path ground truth and quantitatively evaluate the performance of out-of-path rendering, where our method outperforms previous methods by a significant margin.
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