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

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

Applied Sciences ◽

10.3390/app10103409 ◽

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.

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

Remote Sensing ◽

10.3390/rs13010066 ◽

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.

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Processing chain for 3D histogram of gradients based real-time object recognition

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420978363 ◽

2021 ◽

Vol 18 (1) ◽

pp. 172988142097836

Author(s):

Cristian Vilar ◽

Silvia Krug ◽

Benny Thörnberg

Keyword(s):

Deep Learning ◽

Object Recognition ◽

Real Time ◽

Recognition Accuracy ◽

3D Object Recognition ◽

Learning Approaches ◽

Data Set ◽

3D Object ◽

Time Performance ◽

Voxel Grid

3D object recognition has been a cutting-edge research topic since the popularization of depth cameras. These cameras enhance the perception of the environment and so are particularly suitable for autonomous robot navigation applications. Advanced deep learning approaches for 3D object recognition are based on complex algorithms and demand powerful hardware resources. However, autonomous robots and powered wheelchairs have limited resources, which affects the implementation of these algorithms for real-time performance. We propose to use instead a 3D voxel-based extension of the 2D histogram of oriented gradients (3DVHOG) as a handcrafted object descriptor for 3D object recognition in combination with a pose normalization method for rotational invariance and a supervised object classifier. The experimental goal is to reduce the overall complexity and the system hardware requirements, and thus enable a feasible real-time hardware implementation. This article compares the 3DVHOG object recognition rates with those of other 3D recognition approaches, using the ModelNet10 object data set as a reference. We analyze the recognition accuracy for 3DVHOG using a variety of voxel grid selections, different numbers of neurons ( Nh) in the single hidden layer feedforward neural network, and feature dimensionality reduction using principal component analysis. The experimental results show that the 3DVHOG descriptor achieves a recognition accuracy of 84.91% with a total processing time of 21.4 ms. Despite the lower recognition accuracy, this is close to the current state-of-the-art approaches for deep learning while enabling real-time performance.

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