scholarly journals DGCB-Net: Dynamic Graph Convolutional Broad Network for 3D Object Recognition in Point Cloud

2020 ◽  
Vol 13 (1) ◽  
pp. 66
Author(s):  
Yifei Tian ◽  
Long Chen ◽  
Wei Song ◽  
Yunsick Sung ◽  
Sangchul Woo
Keyword(s):  
Object Recognition ◽  
Point Cloud ◽  
Recognition Performance ◽  
Point Clouds ◽  
Disease Diagnosis ◽  
3D Object Recognition ◽  
Dynamic Graph ◽  
3D Object ◽  
Feature Aggregation ◽  
Dimensional Object

3D (3-Dimensional) object recognition is a hot research topic that benefits environment perception, disease diagnosis, and the mobile robot industry. Point clouds collected by range sensors are a popular data structure to represent a 3D object model. This paper proposed a 3D object recognition method named Dynamic Graph Convolutional Broad Network (DGCB-Net) to realize feature extraction and 3D object recognition from the point cloud. DGCB-Net adopts edge convolutional layers constructed by weight-shared multiple-layer perceptrons (MLPs) to extract local features from the point cloud graph structure automatically. Features obtained from all edge convolutional layers are concatenated together to form a feature aggregation. Unlike stacking many layers in-depth, our DGCB-Net employs a broad architecture to extend point cloud feature aggregation flatly. The broad architecture is structured utilizing a flat combining architecture with multiple feature layers and enhancement layers. Both feature layers and enhancement layers concatenate together to further enrich the features’ information of the point cloud. All features work on the object recognition results thus that our DGCB-Net show better recognition performance than other 3D object recognition algorithms on ModelNet10/40 and our scanning point cloud dataset.

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

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.

A Comparative Study of VoxelNet and PointNet for 3D Object Detection in Car by Using KITTI Benchmark

2018 ◽  
Vol 10 (3) ◽  
pp. 28-38
Author(s):  
Harish S Gujjar
Keyword(s):  
Virtual Reality ◽  
Object Recognition ◽  
High Performance ◽  
Point Clouds ◽  
Small Scale ◽  
3D Object Recognition ◽  
3D Object ◽  
Robotic Vision ◽  
Car Detection ◽  
3D Object Detection

In today's world, 2D object recognition is a normal course of study in research. 3D objection recognition is more in demand and important in the present scenario. 3D object recognition has gained importance in areas such as navigation of vehicles, robotic vision, HoME, virtual reality, etc. This work reveals the two important methods, Voxelnet and PointNet, useful in 3D object recognition. In case of NetPoint, the recognition is good when used with segmentation of point clouds which are in small-scale. Whereas, in case of Voxelnet, scans are used directly on raw points of clouds which are directly operated on patterns. The above conclusion is arrived on KITTI car detection. The KITTI uses detection by using bird's eye view. In this method of KITTI we compare two different methods called LiDAR and RGB-D. We arrive at a conclusion that pointNet is useful and has high performance when we are using small scenarios and Voxelnet is useful and has high performance when we are using large scenarios.

scholarly journals Realworld 3D Object Recognition Using a 3D Extension of the HOG Descriptor and a Depth Camera

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 910
Author(s):  
Cristian Vilar ◽  
Silvia Krug ◽  
Mattias O’Nils
Keyword(s):  
Object Recognition ◽  
Autonomous Vehicles ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Real Data ◽  
3D Object Recognition ◽  
Depth Camera ◽  
3D Object ◽  
Real Objects ◽  
Histogram Of Gradients

3D object recognition is an generic task in robotics and autonomous vehicles. In this paper, we propose a 3D object recognition approach using a 3D extension of the histogram-of-gradients object descriptor with data captured with a depth camera. The presented method makes use of synthetic objects for training the object classifier, and classify real objects captured by the depth camera. The preprocessing methods include operations to achieve rotational invariance as well as to maximize the recognition accuracy while reducing the feature dimensionality at the same time. By studying different preprocessing options, we show challenges that need to be addressed when moving from synthetic to real data. The recognition performance was evaluated with a real dataset captured by a depth camera and the results show a maximum recognition accuracy of 81.5%.

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