# HPERL: 3D Human Pose Estimation from RGB and LiDAR

arXiv: https://arxiv.org/abs/2010.08221 · HTML: https://mlsanity.com/paper/2010.08221/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 19 of 19)

- [Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving](https://mlsanity.com/paper/2112.12141/) (2021)
- [Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR](https://mlsanity.com/paper/2211.16951/) (2022)
- [HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving](https://mlsanity.com/paper/2212.07729/) (2022)
- [RELI11D: A Comprehensive Multimodal Human Motion Dataset and Method](https://mlsanity.com/paper/2403.19501/) (2024)
- [Towards Balanced Multi-Modal Learning in 3D Human Pose Estimation](https://mlsanity.com/paper/2501.05264/) (2025)
- [3D Human Keypoints Estimation From Point Clouds in the Wild Without Human Labels](https://mlsanity.com/paper/2306.04745/) (2023)
- [Pre-training a Density-Aware Pose Transformer for Robust LiDAR-based 3D Human Pose Estimation](https://mlsanity.com/paper/2412.13454/) (2024)
- [Human Modelling and Pose Estimation Overview](https://mlsanity.com/paper/2406.19290/) (2024)
- [Improving Real-Time Omnidirectional 3D Multi-Person Human Pose Estimation with People Matching and Unsupervised 2D-3D Lifting](https://mlsanity.com/paper/2403.09437/) (2024)
- [Towards Multi-Modal Animal Pose Estimation: A Survey and In-Depth Analysis](https://mlsanity.com/paper/2410.09312/) (2024)
- [RISE: Single Static Radar-based Indoor Scene Understanding](https://mlsanity.com/paper/2511.14019/) (2025)
- [LiCamPose: Combining Multi-View LiDAR and RGB Cameras for Robust Single-timestamp 3D Human Pose Estimation](https://mlsanity.com/paper/2312.06409/) (2023)
- [P2P-Insole: Human Pose Estimation Using Foot Pressure Distribution and Motion Sensors](https://mlsanity.com/paper/2505.00755/) (2025)
- [Neural Network Based Lidar Gesture Recognition for Realtime Robot Teleoperation](https://mlsanity.com/paper/2109.08263/) (2021)
- [Object Permanence in Object Detection Leveraging Temporal Priors at Inference Time](https://mlsanity.com/paper/2211.15505/) (2022)
- [3D Human Pose and Shape Estimation from LiDAR Point Clouds: A Review](https://mlsanity.com/paper/2509.12197/) (2025)
- [Learning Human-Object Interaction for 3D Human Pose Estimation from LiDAR Point Clouds](https://mlsanity.com/paper/2603.16343/) (2026)
- [Contactless 3D Human Body Measurement Using Depth Cameras for Smart Health Monitoring](https://mlsanity.com/paper/2606.11578/) (2026)
- [Kinematics-Induced Multimodal 3D Human Pose Estimation with Subject-Level Privacy](https://mlsanity.com/paper/2610.02943/) (2026)

## Abstract (arXiv)

In-the-wild human pose estimation has a huge potential for various fields, ranging from animation and action recognition to intention recognition and prediction for autonomous driving. The current state-of-the-art is focused only on RGB and RGB-D approaches for predicting the 3D human pose. However, not using precise LiDAR depth information limits the performance and leads to very inaccurate absolute pose estimation. With LiDAR sensors becoming more affordable and common on robots and autonomous vehicle setups, we propose an end-to-end architecture using RGB and LiDAR to predict the absolute 3D human pose with unprecedented precision. Additionally, we introduce a weakly-supervised approach to generate 3D predictions using 2D pose annotations from PedX [1]. This allows for many new opportunities in the field of 3D human pose estimation.