A 3D object recognition and pose estimation system using deep learning method

2014 ◽  
Author(s):  
Dong Liang ◽  
Kaijian Weng ◽  
Can Wang ◽  
Guoyuan Liang ◽  
Haoyao Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Pose Estimation ◽  
3D Object Recognition ◽  
Learning Method ◽  
3D Object ◽  
Estimation System

scholarly journals 3D Object Recognition and Pose Estimation From Point Cloud Using Stably Observed Point Pair Feature

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 44335-44345 ◽  
Author(s):  
Deping Li ◽  
Hanyun Wang ◽  
Ning Liu ◽  
Xiaoming Wang ◽  
Jin Xu
Keyword(s):  
Object Recognition ◽  
Pose Estimation ◽  
Point Cloud ◽  
3D Object Recognition ◽  
3D Object ◽  
Point Pair

Review of Multi-view 3D Object Recognition Methods Based on Deep Learning

Displays ◽  
2021 ◽  
pp. 102053
Author(s):  
Shaohua Qi ◽  
Xin Ning ◽  
Guowei Yang ◽  
Liping Zhang ◽  
Peng Long ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
3D Object Recognition ◽  
3D Object

scholarly journals Par3DNet: Using 3DCNNs for Object Recognition on Tridimensional Partial Views

2020 ◽  
Vol 10 (10) ◽  
pp. 3409
Author(s):  
Francisco Gomez-Donoso ◽  
Felix Escalona ◽  
Miguel Cazorla
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Point Cloud ◽  
Real Data ◽  
3D Object Recognition ◽  
3D Object ◽  
3D Data

Deep learning-based methods have proven to be the best performers when it comes to object recognition cues both in images and tridimensional data. Nonetheless, when it comes to 3D object recognition, the authors tend to convert the 3D data to images and then perform their classification. However, despite its accuracy, this approach has some issues. In this work, we present a deep learning pipeline for object recognition that takes a point cloud as input and provides the classification probabilities as output. Our proposal is trained on synthetic CAD objects and is able to perform accurately when fed with real data provided by commercial sensors. Unlike most approaches, our method is specifically trained to work on partial views of the objects rather than on a full representation, which is not the representation of the objects as captured by commercial sensors. We trained our proposal with the ModelNet10 dataset and achieved a 78.39 % accuracy. We also tested it by adding noise to the dataset and against a number of datasets and real data with high success.

3D object recognition and pose estimation for random bin-picking using Partition Viewpoint Feature Histograms

2019 ◽  
Vol 128 ◽  
pp. 148-154 ◽  
Author(s):  
Deping Li ◽  
Ning Liu ◽  
Yulan Guo ◽  
Xiaoming Wang ◽  
Jin Xu
Keyword(s):  
Object Recognition ◽  
Pose Estimation ◽  
3D Object Recognition ◽  
3D Object ◽  
Bin Picking
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