用F1赛车摄像头视频重建3D赛道，突破高速运动与镜头切换挑战
VROOM - Visual Reconstruction over Onboard Multiview
- 融合多视角车载视频与SLAM算法，结合动态遮罩与分段处理
- 在蒙特卡洛大奖赛数据上部分还原赛道与车辆轨迹
- 适合自动驾驶、4D场景重建研究者参考
我们提出VROOM系统，仅使用赛车车载摄像头拍摄的2023年蒙特卡洛大奖赛视频，重建一级方程式赛道的3D模型。针对高速运动和镜头剧烈切换等视频难题，系统对比分析了DROID-SLAM、AnyCam和Monst3r等方法，并结合掩码处理、时间分块和分辨率缩放等预处理策略，以应对动态运动与计算资源限制。实验表明，VROOM可在复杂环境中部分恢复赛道结构与车辆轨迹，验证了基于车载视频实现真实场景下可扩展4D重建的可行性。项目主页：https://varun-bharadwaj.github.io/vroom，代码开源：https://github.com/yajatyadav/vroom。
原文摘要 · Abstract (English)
We introduce VROOM, a system for reconstructing 3D models of Formula 1 circuits using only onboard camera footage from racecars. Leveraging video data from the 2023 Monaco Grand Prix, we address video challenges such as high-speed motion and sharp cuts in camera frames. Our pipeline analyzes different methods such as DROID-SLAM, AnyCam, and Monst3r and combines preprocessing techniques such as different methods of masking, temporal chunking, and resolution scaling to account for dynamic motion and computational constraints. We show that Vroom is able to partially recover track and vehicle trajectories in complex environments. These findings indicate the feasibility of using onboard video for scalable 4D reconstruction in real-world settings. The project page can be found at https://varun-bharadwaj.github.io/vroom, and our code is available at https://github.com/yajatyadav/vroom.
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