PedX: Benchmark Dataset for Metric 3D Pose Estimation of Pedestrians in Complex Urban Intersections
This provides a benchmark dataset for metric 3D pose estimation of pedestrians, addressing a domain-specific need in autonomous driving and computer vision.
The authors introduced PedX, a large-scale multimodal dataset with over 5,000 stereo image-LiDAR pairs and 2D/3D pedestrian labels for complex urban intersections, and developed a novel 3D model fitting algorithm for automatic labeling validated with mocap systems.
This paper presents a novel dataset titled PedX, a large-scale multimodal collection of pedestrians at complex urban intersections. PedX consists of more than 5,000 pairs of high-resolution (12MP) stereo images and LiDAR data along with providing 2D and 3D labels of pedestrians. We also present a novel 3D model fitting algorithm for automatic 3D labeling harnessing constraints across different modalities and novel shape and temporal priors. All annotated 3D pedestrians are localized into the real-world metric space, and the generated 3D models are validated using a mocap system configured in a controlled outdoor environment to simulate pedestrians in urban intersections. We also show that the manual 2D labels can be replaced by state-of-the-art automated labeling approaches, thereby facilitating automatic generation of large scale datasets.