ReviewSensors (Basel, Switzerland)2021
A Review: Point Cloud-Based 3D Human Joints Estimation.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- A Fully Automated Deep Learning Pipeline for Anatomical Landmark Localization on Three-Dimensional Pelvic Surface Scans.Sensors (Basel, Switzerland) · 2026Article
- A fast and robust geometric point cloud registration model for orthopedic surgery with noisy and incomplete data.International journal of computer assisted radiology and surgery · 2025Article
- Your turn: At home turning angle estimation for Parkinson's disease severity assessment.Artificial intelligence in medicine · 2025Article
- Bibliometric analysis of the application of artificial intelligence in orthopedic imaging.Quantitative imaging in medicine and surgery · 2025Article
- Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends.Journal of imaging · 2025Review
- mmPose-FK: A Forward Kinematics Approach to Dynamic Skeletal Pose Estimation Using mmWave Radars.IEEE sensors journal · 2024Article
- Survey of methods and principles in three-dimensional reconstruction from two-dimensional medical images.Visual computing for industry, biomedicine, and art · 2023Review
- Editorial: Special Issue "Optical Signal Processing Technologies for Communication, Computing, and Sensing Applications".Sensors (Basel, Switzerland) · 2023Article
- Point cloud completion in challenging indoor scenarios with human motion.Frontiers in robotics and AI · 2023Review
- Human Arm Motion Prediction for Collision Avoidance in a Shared Workspace.Sensors (Basel, Switzerland) · 2022Article
- Accuracy Assessment of Joint Angles Estimated from 2D and 3D Camera Measurements.Sensors (Basel, Switzerland) · 2022Article
- DOPE++: 6D pose estimation algorithm for weakly textured objects based on deep neural networks.PloS one · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Joint estimation of the human body is suitable for many fields such as human-computer interaction, autonomous driving, video analysis and virtual reality. Although many depth-based researches have been classified and generalized in previous review or survey papers, the point cloud-based pose estimation of human body is still difficult due to the disorder and rotation invariance of the point cloud. In this review, we summarize the recent development on the point cloud-based pose estimation of the human body. The existing works are divided into three categories based on their working principles, including template-based method, feature-based method and machine learning-based method. Especially, the significant works are highlighted with a detailed introduction to analyze their characteristics and limitations. The widely used datasets in the field are summarized, and quantitative comparisons are provided for the representative methods. Moreover, this review helps further understand the pertinent applications in many frontier research directions. Finally, we conclude the challenges involved and problems to be solved in future researches.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.