VROOM - Visual Reconstruction over Onboard Multiview

2025-08-23

AI Summary

This work addresses the challenge of monocular 3D reconstruction from high-speed onboard video sequences in Formula 1 racing. To tackle severe ego-motion, abrupt camera cuts, and dynamic illumination changes, we propose a hybrid 4D (3D + time) reconstruction pipeline tailored to dynamic real-world scenes. The method integrates multi-stage preprocessing—including semantic masking, temporal chunking, and resolution-adaptive scaling—with state-of-the-art visual SLAM frameworks (DROID-SLAM, AnyCam, Monst3r). To our knowledge, this is the first end-to-end, scalable 4D reconstruction system operating exclusively on raw vehicle-mounted video, without reliance on external sensors or prior maps. Evaluated on real-world Monaco Grand Prix footage, the system robustly recovers track geometry and centimeter-accurate vehicle trajectories under extreme motion conditions. Results demonstrate strong robustness and practicality in highly dynamic environments, establishing a novel paradigm for motorsport analytics, autonomous driving simulation, and digital twin generation.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Feature Construction/Reformulation
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Reconstructing 3D models from onboard racecar camera footage
• Addressing high-speed motion and sharp camera cuts challenges
• Recovering track and vehicle trajectories in complex environments

Innovation

• Uses onboard camera footage for 3D reconstruction
• Combines SLAM methods with preprocessing techniques
• Addresses high-speed motion through temporal chunking