Graph neural network for 6D object pose estimation

2021 ◽  
Vol 218 ◽  
pp. 106839
Author(s):  
Pengshuai Yin ◽  
Jiayong Ye ◽  
Guoshen Lin ◽  
Qingyao Wu
Keyword(s):  
Neural Network ◽  
Pose Estimation ◽  
Object Pose Estimation

Efficient representation and feature extraction for neural network-based 3D object pose estimation

2013 ◽  
Vol 120 ◽  
pp. 90-100 ◽  
Author(s):  
Rigas Kouskouridas ◽  
Antonios Gasteratos ◽  
Christos Emmanouilidis
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Pose Estimation ◽  
3D Object ◽  
Object Pose Estimation ◽  
Efficient Representation

scholarly journals RobotP: A Benchmark Dataset for 6D Object Pose Estimation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1299
Author(s):  
Honglin Yuan ◽  
Tim Hoogenkamp ◽  
Remco C. Veltkamp
Keyword(s):  
Pose Estimation ◽  
Ground Truth ◽  
3D Models ◽  
Depth Image ◽  
Great Success ◽  
Estimation Algorithms ◽  
Depth Images ◽  
Object Pose Estimation ◽  
Image Pairs ◽  
Bounding Boxes

Deep learning has achieved great success on robotic vision tasks. However, when compared with other vision-based tasks, it is difficult to collect a representative and sufficiently large training set for six-dimensional (6D) object pose estimation, due to the inherent difficulty of data collection. In this paper, we propose the RobotP dataset consisting of commonly used objects for benchmarking in 6D object pose estimation. To create the dataset, we apply a 3D reconstruction pipeline to produce high-quality depth images, ground truth poses, and 3D models for well-selected objects. Subsequently, based on the generated data, we produce object segmentation masks and two-dimensional (2D) bounding boxes automatically. To further enrich the data, we synthesize a large number of photo-realistic color-and-depth image pairs with ground truth 6D poses. Our dataset is freely distributed to research groups by the Shape Retrieval Challenge benchmark on 6D pose estimation. Based on our benchmark, different learning-based approaches are trained and tested by the unified dataset. The evaluation results indicate that there is considerable room for improvement in 6D object pose estimation, particularly for objects with dark colors, and photo-realistic images are helpful in increasing the performance of pose estimation algorithms.

HOT-Net: Non-Autoregressive Transformer for 3D Hand-Object Pose Estimation

2020 ◽  
Author(s):  
Lin Huang ◽  
Jianchao Tan ◽  
Jingjing Meng ◽  
Ji Liu ◽  
Junsong Yuan
Keyword(s):  
Pose Estimation ◽  
Object Pose Estimation

scholarly journals Deep convolutional neural network-based Bernoulli heatmap for head pose estimation

2021 ◽  
Vol 436 ◽  
pp. 198-209
Author(s):  
Zhongxu Hu ◽  
Yang Xing ◽  
Chen Lv ◽  
Peng Hang ◽  
Jie Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pose Estimation ◽  
Deep Convolutional Neural Network ◽  
Head Pose Estimation ◽  
Head Pose
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