PedX: Benchmark Dataset for Metric 3D Pose Estimation of Pedestrians in Complex Urban Intersections

TL;DR

The paper introduces PedX, a large-scale multimodal dataset with high-resolution images, LiDAR data, and 3D labels of pedestrians in urban intersections, along with a novel 3D labeling algorithm and validation methods.

Contribution

It provides a comprehensive dataset and a new automatic 3D labeling method for pedestrians in complex urban environments, enabling advanced research in 3D pose estimation.

Findings

PedX dataset contains over 5,000 annotated pedestrian instances.

The proposed 3D model fitting algorithm effectively automates labeling across modalities.

Validation shows high accuracy of 3D models in real-world metric space.

Abstract

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.