mlsanityData to 7 Oct 2026

Multi-Person 3D Pose Estimation from Multi-View Uncalibrated Depth Cameras

arXiv 2401.15616 ↗First posted 28 Jan 2024cs.CVcited by 6

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Abstract (arXiv)

We tackle the task of multi-view, multi-person 3D human pose estimation from a limited number of uncalibrated depth cameras. Recently, many approaches have been proposed for 3D human pose estimation from multi-view RGB cameras. However, these works (1) assume the number of RGB camera views is large enough for 3D reconstruction, (2) the cameras are calibrated, and (3) rely on ground truth 3D poses for training their regression model. In this work, we propose to leverage sparse, uncalibrated depth cameras providing RGBD video streams for 3D human pose estimation. We present a simple pipeline for Multi-View Depth Human Pose Estimation (MVD-HPE) for jointly predicting the camera poses and 3D human poses without training a deep 3D human pose regression model. This framework utilizes 3D Re-ID appearance features from RGBD images to formulate more accurate correspondences (for deriving camera positions) compared to using RGB-only features. We further propose (1) depth-guided camera-pose estimation by leveraging 3D rigid transformations as guidance and (2) depth-constrained 3D human pose estimation by utilizing depth-projected 3D points as an alternative objective for optimization. In order to evaluate our proposed pipeline, we collect three video sets of RGBD videos recorded from multiple sparse-view depth cameras and ground truth 3D poses are manually annotated. Experiments show that our proposed method outperforms the current 3D human pose regression-free pipelines in terms of both camera pose estimation and 3D human pose estimation.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 36 of its 67 references that are in the map. First posted on arXiv 28 Jan 2024.

Builds on

36 of 67 references.

References · 36

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24 more
  1. 2017Single-Shot Multi-Person 3D Pose Estimation From Monocular RGBcited by 195
  2. 2018LF-Net: Learning Local Features from Imagescited by 151
  3. 2019Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Imagecited by 131
  4. 20163D Human Pose Estimation = 2D Pose Estimation + Matchingcited by 112
  5. 2018Ordinal Depth Supervision for 3D Human Pose Estimationcited by 107
  6. 2019Single-Stage Multi-Person Pose Machinescited by 95
  7. 2019Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density Networkcited by 92
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  17. 2020Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimationcited by 36
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  19. 2019Introduction to Camera Pose Estimation with Deep Learningcited by 25
  20. 2021PandaNet : Anchor-Based Single-Shot Multi-Person 3D Pose Estimationcited by 22
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  24. 2019Estimating 3D Camera Pose from 2D Pedestrian Trajectoriescited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 4.

6 papers.

  1. 2024Segment Anything Model for Road Network Graph Extractioncited by 28
  2. 2025Spatiotemporal Multi-Camera Calibration using Freely Moving Peoplecited by 3
  3. 2026COMPOSE: Hypergraph Cover Optimization for Multi-view 3D Human Pose Estimationcited by 1
  4. 2026Unconstrained Multi-view Human Pose Estimation with Algebraic Priorscited by 1
  5. 2026HARMONI: Aligning Human and Scene Priors for Multi-View 4D Reconstructioncited by 0
  6. 2026Feed-Forward Multi-view Multi-person Reconstruction with Contrastive Human-Aware 3D Representationcited by 0