3D object recognition from large-scale point clouds with global descriptor and sliding window

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.

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Multi-Scale Point-Wise Convolutional Neural Networks for 3D Object Segmentation From LiDAR Point Clouds in Large-Scale Environments

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2019.2961060 ◽

2020 ◽

pp. 1-16 ◽

Cited By ~ 2

Author(s):

Lingfei Ma ◽

Ying Li ◽

Jonathan Li ◽

Weikai Tan ◽

Yongtao Yu ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Large Scale ◽

Object Segmentation ◽

Point Clouds ◽

3D Object ◽

Multi Scale ◽

Scale Point ◽

3D Object Segmentation

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Learning high-level features by fusing multi-view representation of MLS point clouds for 3D object recognition in road environments

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2019.01.024 ◽

2019 ◽

Vol 150 ◽

pp. 44-58 ◽

Cited By ~ 7

Author(s):

Zhipeng Luo ◽

Jonathan Li ◽

Zhenlong Xiao ◽

Z. Geroge Mou ◽

Xiaojie Cai ◽

...

Keyword(s):

Object Recognition ◽

Point Clouds ◽

3D Object Recognition ◽

3D Object ◽

High Level

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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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