6D Object Pose Estimation Algorithm Using Preprocessing of Segmentation and Keypoint Extraction

Zhang, Yan; Zhang, Chen; Rosenberger, Maik; Notni, Gunther

doi:10.1109/i2mtc43012.2020.9128980

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…In [ 18 ], transfer learning of YOLO [ 1 ], a deep learning model, was performed on thermal images for human detection in a night environment. In [ 19 ], YOLO was applied on 6-channel 2D images containing RGB color and various geometric features (point density, difference of normal, and curvature). A further CNN (convolutional neural network) model named MCNet for 2D thermal image semantic segmentation of nighttime driving scenes was published in [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Point Cloud Hand–Object Segmentation Using Multimodal Imaging with Thermal and Color Data for Safe Robotic Object Handover

Zhang

Müller

Stephan

et al. 2021

Sensors

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This paper presents an application of neural networks operating on multimodal 3D data (3D point cloud, RGB, thermal) to effectively and precisely segment human hands and objects held in hand to realize a safe human–robot object handover. We discuss the problems encountered in building a multimodal sensor system, while the focus is on the calibration and alignment of a set of cameras including RGB, thermal, and NIR cameras. We propose the use of a copper–plastic chessboard calibration target with an internal active light source (near-infrared and visible light). By brief heating, the calibration target could be simultaneously and legibly captured by all cameras. Based on the multimodal dataset captured by our sensor system, PointNet, PointNet++, and RandLA-Net are utilized to verify the effectiveness of applying multimodal point cloud data for hand–object segmentation. These networks were trained on various data modes (XYZ, XYZ-T, XYZ-RGB, and XYZ-RGB-T). The experimental results show a significant improvement in the segmentation performance of XYZ-RGB-T (mean Intersection over Union: 82.8% by RandLA-Net) compared with the other three modes (77.3% by XYZ-RGB, 35.7% by XYZ-T, 35.7% by XYZ), in which it is worth mentioning that the Intersection over Union for the single class of hand achieves 92.6%.

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Section: Related Workmentioning

confidence: 99%

Point Cloud Hand–Object Segmentation Using Multimodal Imaging with Thermal and Color Data for Safe Robotic Object Handover

Zhang

Müller

Stephan

et al. 2021

Sensors

Self Cite

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6D Pose Estimation for Precision Assembly

Skeik

Erden

Kong

2022

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)

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6D Pose Estimation of Objects: Recent Technologies and Challenges

Feng

Zhao

et al. 2020

Applied Sciences

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6D pose estimation is a common and important task in industry. Obtaining the 6D pose of objects is the basis for many other functions such as bin picking, autopilot, etc. Therefore, many corresponding studies have been made in order to improve the accuracy and enlarge the range of application of various approaches. After several years of development, the methods of 6D pose estimation have been enriched and improved. Although some predecessors have analyzed the methods and summarized them in detailed, there have been many new breakthroughs in recent years. To understand 6D pose estimation better, this paper will make a new and more detailed review of 6D pose estimation. We divided these methods into two approaches: Learning-based approaches and non-learning-based approaches, including 2D-information-based approach and 3D-information-based approach. Additionally, we introduce the challenges that exist in 6D pose estimation. Finally, we compare the performance of different methods qualitatively and discuss the future development trends of the 6D pose estimation.

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6D Object Pose Estimation Algorithm Using Preprocessing of Segmentation and Keypoint Extraction

Cited by 3 publications

References 16 publications

Point Cloud Hand–Object Segmentation Using Multimodal Imaging with Thermal and Color Data for Safe Robotic Object Handover

Point Cloud Hand–Object Segmentation Using Multimodal Imaging with Thermal and Color Data for Safe Robotic Object Handover

6D Pose Estimation for Precision Assembly

6D Pose Estimation of Objects: Recent Technologies and Challenges

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