ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Driving

• URL: http://arxiv.org/abs/2508.13977v1
• Date: Tue, 19 Aug 2025 16:13:49 GMT
• Title: ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Driving
• Authors: Xianda Guo, Ruijun Zhang, Yiqun Duan, Ruilin Wang, Keyuan Zhou, Wenzhao Zheng, Wenke Huang, Gangwei Xu, Mike Horton, Yuan Si, Hao Zhao, Long Chen,
• Abstract summary: We introduce a large-scale, diverse, frame-wise continuous dataset for depth estimation in dynamic outdoor driving environments.<n>Compared to existing datasets, ours presents greater diversity in driving scenarios and lower depth density, creating new challenges for generalization.
• Score: 16.84661057744478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depth estimation is a fundamental task for 3D scene understanding in autonomous driving, robotics, and augmented reality. Existing depth datasets, such as KITTI, nuScenes, and DDAD, have advanced the field but suffer from limitations in diversity and scalability. As benchmark performance on these datasets approaches saturation, there is an increasing need for a new generation of large-scale, diverse, and cost-efficient datasets to support the era of foundation models and multi-modal learning. To address these challenges, we introduce a large-scale, diverse, frame-wise continuous dataset for depth estimation in dynamic outdoor driving environments, comprising 20K video frames to evaluate existing methods. Our lightweight acquisition pipeline ensures broad scene coverage at low cost, while sparse yet statistically sufficient ground truth enables robust training. Compared to existing datasets, ours presents greater diversity in driving scenarios and lower depth density, creating new challenges for generalization. Benchmark experiments with standard monocular depth estimation models validate the dataset's utility and highlight substantial performance gaps in challenging conditions, establishing a new platform for advancing depth estimation research.

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