A Review: Point Cloud-Based 3D Human Joints Estimation

Xu, Tianxu; An, Dong; Jia, Yuetong; Yue, Yang

doi:10.3390/s21051684

Cited by 31 publications

(22 citation statements)

References 109 publications

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“…For clarity and conciseness, they are commonly called Kinect V1 and Kinect V2 respectively). More datasets with depth and annotated keypoints became available [10], facilitating further research in the field. The Kinect V1 algorithm uses trained classifiers to segment different body parts in the depth image and then derive the keypoints from them [11].…”

Section: Related Workmentioning

confidence: 99%

Accuracy Assessment of Joint Angles Estimated from 2D and 3D Camera Measurements

Crombrugge

Sels

Ribbens

et al. 2022

Sensors

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To automatically evaluate the ergonomics of workers, 3D skeletons are needed. Most ergonomic assessment methods, like REBA, are based on the different 3D joint angles. Thanks to the huge amount of training data, 2D skeleton detectors have become very accurate. In this work, we test three methods to calculate 3D skeletons from 2D detections: using the depth from a single RealSense range camera, triangulating the joints using multiple cameras, and combining the triangulation of multiple camera pairs. We tested the methods using recordings of a person doing different assembly tasks. We compared the resulting joint angles to the ground truth of a VICON marker-based tracking system. The resulting RMS angle error for the triangulation methods is between 12° and 16°, showing that they are accurate enough to calculate a useful ergonomic score from.

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Section: Related Workmentioning

confidence: 99%

Accuracy Assessment of Joint Angles Estimated from 2D and 3D Camera Measurements

Crombrugge

Sels

Ribbens

et al. 2022

Sensors

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show abstract

“…This is followed by estimating the relative displacement between them, based on the knowledge of the spatial position of feature points [23,24]. Recent papers recommended using feature-based three-dimensional alignment [25][26][27][28].…”

Section: Related Workmentioning

confidence: 99%

ISVD-Based Advanced Simultaneous Localization and Mapping (SLAM) Algorithm for Mobile Robots

Somlyai

Vamossy

2022

Machines

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In the case of simultaneous localization and mapping, route planning and navigation are based on data captured by multiple sensors, including built-in cameras. Nowadays, mobile devices frequently have more than one camera with overlapping fields of view, leading to solutions where depth information can also be gathered along with ordinary RGB color data. Using these RGB-D sensors, two- and three-dimensional point clouds can be recorded from the mobile devices, which provide additional information for localization and mapping. The method of matching point clouds during the movement of the device is essential: reducing noise while having an acceptable processing time is crucial for a real-life application. In this paper, we present a novel ISVD-based method for displacement estimation, using key points detected by SURF and ORB feature detectors. The ISVD algorithm is a fitting procedure based on SVD resolution, which removes outliers from the point clouds to be fitted in several steps. The developed method removes these outlying points in several steps, in each iteration examining the relative error of the point pairs and then progressively reducing the maximum error for the next matching step. An advantage over relevant methods is that this method always gives the same result, as no random steps are included.

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“…Image-based 6D target pose estimation [ 1 ] is playing an increasingly important role in applications such as virtual reality [ 2 – 4 ] and robotics operations [ 5 – 7 ]. 6D pose estimation refers to finding the image object position and calculating the rotation translation relationship between the object coordinate system and the camera coordinate system.…”

Section: Introductionmentioning

confidence: 99%

DOPE++: 6D pose estimation algorithm for weakly textured objects based on deep neural networks

Jin

Li²,

Zhang³

2022

PLoS ONE

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This paper focuses on 6D pose estimation for weakly textured targets from RGB-D images. A 6D pose estimation algorithm (DOPE++) based on a deep neural network for weakly textured objects is proposed to solve the poor real-time pose estimation and low recognition efficiency in the robot grasping process of parts with weak texture. More specifically, we first introduce the depthwise separable convolution operation to lighten the original deep object pose estimation (DOPE) network structure to improve the network operation speed. Second, an attention mechanism is introduced to improve network accuracy. In response to the low recognition efficiency of the original DOPE network for parts with occlusion relationships and the false recognition problem in recognizing parts with scales that are too large or too small, a random mask local processing method and a multiscale fusion pose estimation module are proposed. The results show that our proposed DOPE++ network improves the real-time performance of 6D pose estimation and enhances the recognition of parts at different scales without loss of accuracy. To address the problem of a single background representation of the part pose estimation dataset, a virtual dataset is constructed for data expansion to form a hybrid dataset.

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A Review: Point Cloud-Based 3D Human Joints Estimation

Cited by 31 publications

References 109 publications

Accuracy Assessment of Joint Angles Estimated from 2D and 3D Camera Measurements

Accuracy Assessment of Joint Angles Estimated from 2D and 3D Camera Measurements

ISVD-Based Advanced Simultaneous Localization and Mapping (SLAM) Algorithm for Mobile Robots

DOPE++: 6D pose estimation algorithm for weakly textured objects based on deep neural networks

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