MV2: Multi-View Multi-Vehicle Driving Dataset for Novel View Synthesis

• URL: http://arxiv.org/abs/2608.12442v2
• Date: Fri, 14 Aug 2026 22:47:20 GMT
• Title: MV2: Multi-View Multi-Vehicle Driving Dataset for Novel View Synthesis
• Abstract summary: Differentiable rendering has advanced novel view synthesis (NVS)<n>Applying it to real-world driving remains difficult due to sparse capture viewpoints, dynamic objects, and limited multi-trajectory data.<n>We introduce the Multi-View Multi-Vehicle dataset and benchmark for evaluating NVS models under large viewpoint changes in dynamic urban scenes.
• Score: 17.447689425723045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Differentiable rendering has advanced novel view synthesis (NVS), yet applying it to real-world driving remains difficult due to sparse capture viewpoints, dynamic objects, and limited multi-trajectory data. We introduce the Multi-View Multi-Vehicle (MV2) dataset and benchmark for evaluating NVS models under large viewpoint changes in dynamic urban scenes. MV2 features synchronized captures from a car, scooter, and drone, each following distinct yet synchronized trajectories. Training NVS methods on one vehicle's camera stream and testing on another enables evaluation under substantially larger viewpoint variations than existing single-trajectory datasets. All sequences are registered via Structure-from-Motion and camera poses verified using manual pixel-level correspondence annotations, yielding 50 high-quality scenes with 12000 images. Benchmarking recent NVS and camera pose estimation methods shows that NVS performance degrades with increasing viewpoint disparity, and that feed-forward pose estimators notably lag behind optimization-based approaches, highlighting MV2 as a rigorous testbed for NVS in driving. The dataset, benchmark protocol, and project resources are available at https://mv2-dataset.github.io/.

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