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

arXiv: https://arxiv.org/abs/1809.03605 · HTML: https://mlsanity.com/paper/1809.03605/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 27 of 27)

- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors](https://mlsanity.com/paper/2205.15410/) (2022)
- [HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR](https://mlsanity.com/paper/2203.09215/) (2022)
- [STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes](https://mlsanity.com/paper/2204.01026/) (2022)
- [LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Clouds](https://mlsanity.com/paper/2203.14698/) (2022)
- [CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-scene Interactions](https://mlsanity.com/paper/2303.17948/) (2023)
- [HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving](https://mlsanity.com/paper/2212.07729/) (2022)
- [LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment](https://mlsanity.com/paper/2402.17171/) (2024)
- [Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review](https://mlsanity.com/paper/2308.06419/) (2023)
- [PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions](https://mlsanity.com/paper/2112.02604/) (2021)
- [RELI11D: A Comprehensive Multimodal Human Motion Dataset and Method](https://mlsanity.com/paper/2403.19501/) (2024)
- [Pedestrian 3D Bounding Box Prediction](https://mlsanity.com/paper/2206.14195/) (2022)
- [Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor Scenes](https://mlsanity.com/paper/2308.00628/) (2023)
- [3D Human Keypoints Estimation From Point Clouds in the Wild Without Human Labels](https://mlsanity.com/paper/2306.04745/) (2023)
- [Self-supervised 3D Human Pose Estimation from a Single Image](https://mlsanity.com/paper/2304.02349/) (2023)
- [Weakly Supervised Multi-Modal 3D Human Body Pose Estimation for Autonomous Driving](https://mlsanity.com/paper/2307.14889/) (2023)
- [Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction](https://mlsanity.com/paper/2401.06757/) (2024)
- [HiSC4D: Human-centered interaction and 4D Scene Capture in Large-scale Space Using Wearable IMUs and LiDAR](https://mlsanity.com/paper/2409.04398/) (2024)
- [A Flexible Multi-view Multi-modal Imaging System for Outdoor Scenes](https://mlsanity.com/paper/2302.10465/) (2023)
- [Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach](https://mlsanity.com/paper/2411.13302/) (2024)
- [DreamPose3D: Hallucinative Diffusion with Prompt Learning for 3D Human Pose Estimation](https://mlsanity.com/paper/2511.09502/) (2025)
- [What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies](https://mlsanity.com/paper/2507.06513/) (2025)
- [3D Human Pose and Shape Estimation from LiDAR Point Clouds: A Review](https://mlsanity.com/paper/2509.12197/) (2025)
- [Sparkle: A Robust and Versatile Representation for Point Cloud based Human Motion Capture](https://mlsanity.com/paper/2604.00857/) (2026)
- [Enhancing Domain Generalization in 3D Human Pose Estimation through Controllable Generative Augmentation](https://mlsanity.com/paper/2605.12198/) (2026)
- [PEDESTRIANQA: A Benchmark for Vision-Language Models on Pedestrian Intention and Trajectory Prediction](https://mlsanity.com/paper/2605.24562/) (2026)
- [Sen-Cap: Sensor-Flexible and Noise-Resilient Human Motion Capture via LiDAR-Camera Integration](https://mlsanity.com/paper/2608.02285/) (2026)

## Abstract (arXiv)

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.