# Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR

arXiv: https://arxiv.org/abs/2211.16951 · HTML: https://mlsanity.com/paper/2211.16951/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 58 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 30 of 48)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016)
- [TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers](https://mlsanity.com/paper/2203.11496/) (2022)
- [PointPainting: Sequential Fusion for 3D Object Detection](https://mlsanity.com/paper/1911.10150/) (2019)
- [A simple yet effective baseline for 3d human pose estimation](https://mlsanity.com/paper/1705.03098/) (2017)
- [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://mlsanity.com/paper/2104.09224/) (2021)
- [Joint 3D Proposal Generation and Object Detection from View Aggregation](https://mlsanity.com/paper/1712.02294/) (2017)
- [HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2011.14672/) (2020)
- [3D Human Pose Estimation with Spatial and Temporal Transformers](https://mlsanity.com/paper/2103.10455/) (2021)
- [Deep Continuous Fusion for Multi-Sensor 3D Object Detection](https://mlsanity.com/paper/2012.10992/) (2020)
- [Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose](https://mlsanity.com/paper/1611.07828/) (2016)
- [Monocular, One-stage, Regression of Multiple 3D People](https://mlsanity.com/paper/2008.12272/) (2020)
- [Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB](https://mlsanity.com/paper/1712.03453/) (2017)
- [MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video](https://mlsanity.com/paper/2203.00859/) (2022)
- [Putting People in their Place: Monocular Regression of 3D People in Depth](https://mlsanity.com/paper/2112.08274/) (2021)
- [Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/1907.11346/) (2019)
- [Exploiting temporal context for 3D human pose estimation in the wild](https://mlsanity.com/paper/1905.04266/) (2019)
- [Self-Supervised Learning of 3D Human Pose using Multi-view Geometry](https://mlsanity.com/paper/1903.02330/) (2019)
- [Unsupervised 3D Pose Estimation with Geometric Self-Supervision](https://mlsanity.com/paper/1904.04812/) (2019)
- [LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images](https://mlsanity.com/paper/1803.00455/) (2018)
- [Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views](https://mlsanity.com/paper/1901.04111/) (2019)
- [SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds](https://mlsanity.com/paper/2004.02774/) (2020)
- [Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image](https://mlsanity.com/paper/1701.00295/) (2017)

## Built on by (most cited first, 23 of 23)

- [MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing](https://mlsanity.com/paper/2305.10345/) (2023)
- [Learning to Adapt SAM for Segmenting Cross-domain Point Clouds](https://mlsanity.com/paper/2310.08820/) (2023)
- [RELI11D: A Comprehensive Multimodal Human Motion Dataset and Method](https://mlsanity.com/paper/2403.19501/) (2024)
- [A Unified Diffusion Framework for Scene-aware Human Motion Estimation from Sparse Signals](https://mlsanity.com/paper/2404.04890/) (2024)
- [LiDAR-HMR: 3D Human Mesh Recovery from LiDAR](https://mlsanity.com/paper/2311.11971/) (2023)
- [DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous Driving](https://mlsanity.com/paper/2407.15661/) (2024)
- [LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network](https://mlsanity.com/paper/2306.12525/) (2023)
- [WildRefer: 3D Object Localization in Large-scale Dynamic Scenes with Multi-modal Visual Data and Natural Language](https://mlsanity.com/paper/2304.05645/) (2023)
- [MOVIN: Real-time Motion Capture using a Single LiDAR](https://mlsanity.com/paper/2309.09314/) (2023)
- [ViBES: A Conversational Agent with Behaviorally-Intelligent 3D Virtual Body](https://mlsanity.com/paper/2512.14234/) (2025)
- [Pre-training a Density-Aware Pose Transformer for Robust LiDAR-based 3D Human Pose Estimation](https://mlsanity.com/paper/2412.13454/) (2024)
- [A Unified Framework for Human-centric Point Cloud Video Understanding](https://mlsanity.com/paper/2403.20031/) (2024)
- [FreeCap: Hybrid Calibration-Free Motion Capture in Open Environments](https://mlsanity.com/paper/2411.04469/) (2024)
- [ClimbingCap: Multi-Modal Dataset and Method for Rock Climbing in World Coordinate](https://mlsanity.com/paper/2503.21268/) (2025)
- [HUNTER: Unsupervised Human-centric 3D Detection via Transferring Knowledge from Synthetic Instances to Real Scenes](https://mlsanity.com/paper/2403.02769/) (2024)
- [Sports Analysis and VR Viewing System Based on Player Tracking and Pose Estimation with Multimodal and Multiview Sensors](https://mlsanity.com/paper/2405.01112/) (2024)
- [Towards Practical Human Motion Prediction with LiDAR Point Clouds](https://mlsanity.com/paper/2408.08202/) (2024)
- [LiCamPose: Combining Multi-View LiDAR and RGB Cameras for Robust Single-timestamp 3D Human Pose Estimation](https://mlsanity.com/paper/2312.06409/) (2023)
- [ELMO: Enhanced Real-time LiDAR Motion Capture through Upsampling](https://mlsanity.com/paper/2410.06963/) (2024)
- [Sixth-Sense: Self-Supervised Learning of Spatial Awareness of Humans from a Planar Lidar](https://mlsanity.com/paper/2502.21029/) (2025)
- [Domain Adaptation-Based Crossmodal Knowledge Distillation for 3D Semantic Segmentation](https://mlsanity.com/paper/2509.00379/) (2025)
- [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)

## Abstract (arXiv)

Depth estimation is usually ill-posed and ambiguous for monocular camera-based 3D multi-person pose estimation. Since LiDAR can capture accurate depth information in long-range scenes, it can benefit both the global localization of individuals and the 3D pose estimation by providing rich geometry features. Motivated by this, we propose a monocular camera and single LiDAR-based method for 3D multi-person pose estimation in large-scale scenes, which is easy to deploy and insensitive to light. Specifically, we design an effective fusion strategy to take advantage of multi-modal input data, including images and point cloud, and make full use of temporal information to guide the network to learn natural and coherent human motions. Without relying on any 3D pose annotations, our method exploits the inherent geometry constraints of point cloud for self-supervision and utilizes 2D keypoints on images for weak supervision. Extensive experiments on public datasets and our newly collected dataset demonstrate the superiority and generalization capability of our proposed method.