mlsanityData to 14 Oct 2026

VROOM - Visual Reconstruction over Onboard Multiview

arXiv 2508.17172 ↗First posted 24 Aug 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 11 references that are in the map. First posted on arXiv 24 Aug 2025.

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8 of 11 references.

References · 8

  1. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,415
  2. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 988
  3. 2020ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAMcited by 878
  4. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  5. 2021DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Camerascited by 687
  6. 2024MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motioncited by 442
  7. 2024MegaSaM: Accurate, Fast, and Robust Structure and Motion from Casual Dynamic Videoscited by 228
  8. 2025UniK3D: Universal Camera Monocular 3D Estimationcited by 62

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