A PointNet-Based Solution for 3D Hand Gesture Recognition

Mirsu, Radu; Simion, Georgiana; Căleanu, Cătălin Daniel; Pop-Calimanu, Ioana Monica

doi:10.3390/s20113226

Cited by 13 publications

(9 citation statements)

References 21 publications

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“…Many affordable RGB-D cameras are widely available today, accelerating research related to 3D reconstruction [ 20 , 21 , 22 , 23 ]. The most widely used commercially available cameras are the Orbbec Astra (Orbbec, Troy, MI, U.S.) series [ 24 ], the Microsoft Kinect Azure [ 25 ] (Microsoft, Redmond, WA, U.S.), and the Intel RealSense L515 [ 26 ] and Intel RealSense D400 series [ 18 ] (Intel, Santa Clara, CA, U.S.).…”

Section: Related Workmentioning

confidence: 99%

Multiple Sensor Synchronization with theRealSense RGB-D Camera

Yoon

Jang

Huh

et al. 2021

Sensors

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When reconstructing a 3D object, it is difficult to obtain accurate 3D geometric information using a single camera. In order to capture detailed geometric information of a 3D object, it is inevitable to increase the number of cameras to capture the object. However, cameras need to be synchronized in order to simultaneously capture frames. If cameras are incorrectly synchronized, many artifacts are produced in the reconstructed 3D object. The RealSense RGB-D camera, which is commonly used for obtaining geometric information of a 3D object, provides synchronization modes to mitigate synchronization errors. However, the synchronization modes provided by theRealSense cameras can only sync depth cameras and have limitations in the number of cameras that can be synchronized using a single host due to the hardware issue of stable data transmission. Therefore, in this paper, we propose a novel synchronization method that synchronizes an arbitrary number of RealSense cameras by adjusting the number of hosts to support stable data transmission. Our method establishes a master–slave architecture in order to synchronize the system clocks of the hosts. While synchronizing the system clocks, delays that resulted from the process of synchronization were estimated so that the difference between the system clocks could be minimized. Through synchronization of the system clocks, cameras connected to the different hosts can be synchronized based on the timestamp of the data received by the hosts. Thus, our method synchronizes theRealSense cameras to simultaneously capture accurate 3D information of an object at a constant frame rate without dropping it.

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Section: Related Workmentioning

confidence: 99%

Multiple Sensor Synchronization with theRealSense RGB-D Camera

Yoon

Jang

Huh

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…This transformation will inevitably lose some of the spatial information of the point cloud, meaning that the deep neural network cannot learn the characteristics of the point cloud well and cannot distinguish different point clouds clearly, which will affect the classification, segmentation, and feature extraction of point clouds in different application environments [33]. Although PointNet can directly process the geometric features of the point cloud, it cannot capture the local information in the point cloud model because it only extracts the features of independent points, leading to the network having a poor ability to extract features from the point cloud, which also affects the generalization ability of the network [34]. DGCNN is based on the graph neural network (GNN) with the help of the concept of CNN in deep learning.…”

Section: Introductionmentioning

confidence: 99%

Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

Zhao

Guo

et al. 2021

IJGI

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Point cloud data can accurately and intuitively reflect the spatial relationship between the coal wall and underground fully mechanized mining equipment. However, the indirect method of point cloud feature extraction based on deep neural networks will lose some of the spatial information of the point cloud, while the direct method will lose some of the local information of the point cloud. Therefore, we propose the use of dynamic graph convolution neural network (DGCNN) to extract the geometric features of the sphere in the point cloud of the fully mechanized mining face (FMMF) in order to obtain the position of the sphere (marker) in the point cloud of the FMMF, thus providing a direct basis for the subsequent transformation of the FMMF coordinates to the national geodetic coordinates with the sphere as the intermediate medium. Firstly, we completed the production of a diversity sphere point cloud (training set) and an FMMF point cloud (test set). Secondly, we further improved the DGCNN to enhance the effect of extracting the geometric features of the sphere in the FMMF. Finally, we compared the effect of the improved DGCNN with that of PointNet and PointNet++. The results show the correctness and feasibility of using DGCNN to extract the geometric features of point clouds in the FMMF and provide a new method for the feature extraction of point clouds in the FMMF. At the same time, the results provide a direct early guarantee for analyzing the point cloud data of the FMMF under the national geodetic coordinate system in the future. This can provide an effective basis for the straightening and inclining adjustment of scraper conveyors, and it is of great significance for the transparent, unmanned, and intelligent mining of the FMMF.

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“…The main tasks of CV are acquiring, processing, analyzing, and understanding the environment through digital images [ 1 ]. Some high-level problems which are successfully tackled by CV are optical character recognition (OCR) [ 2 , 3 , 4 ], machine vision inspection [ 5 , 6 ], 3D model building (photogrammetry) [ 7 , 8 ], medical imaging [ 9 , 10 ], automotive safety [ 11 ], motion capture, surveillance [ 12 ], fingerprint recognition [ 13 , 14 ], face recognition, and gesture recognition [ 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

Orhei

Vert

Mocofan

et al. 2021

Sensors

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Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow.

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A PointNet-Based Solution for 3D Hand Gesture Recognition

Cited by 13 publications

References 21 publications

Multiple Sensor Synchronization with theRealSense RGB-D Camera

Multiple Sensor Synchronization with theRealSense RGB-D Camera

Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN

End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

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