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 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.

3D object recognition method with multiple feature extraction from LiDAR point clouds

2019 ◽  
Vol 75 (8) ◽  
pp. 4430-4442 ◽  
Author(s):  
Yifei Tian ◽  
Wei Song ◽  
Su Sun ◽  
Simon Fong ◽  
Shuanghui Zou
Keyword(s):  
Feature Extraction ◽  
Object Recognition ◽  
Point Clouds ◽  
3D Object Recognition ◽  
Recognition Method ◽  
3D Object ◽  
Multiple Feature

3D object recognition for Virtual Reality based Digital Twins

2021 ◽  
Author(s):  
Ilyas Ashkir ◽  
Ben Roullier ◽  
Frank McQuade ◽  
Ashiq Anjum
Keyword(s):  
Virtual Reality ◽  
Object Recognition ◽  
3D Object Recognition ◽  
3D Object ◽  
Digital Twins

scholarly journals Efficient 3D object recognition using foveated point clouds

2013 ◽  
Vol 37 (5) ◽  
pp. 496-508 ◽  
Author(s):  
Rafael Beserra Gomes ◽  
Bruno Marques Ferreira da Silva ◽  
Lourena Karin de Medeiros Rocha ◽  
Rafael Vidal Aroca ◽  
Luiz Carlos Pacheco Rodrigues Velho ◽  
...  
Keyword(s):  
Object Recognition ◽  
Point Clouds ◽  
3D Object Recognition ◽  
3D Object

scholarly journals Strong-Weak Feature Alignment for 3D Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1205
Author(s):  
Zhiyu Wang ◽  
Li Wang ◽  
Bin Dai
Keyword(s):  
Object Detection ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Feature Representation ◽  
Alignment Algorithm ◽  
3D Object ◽  
3D Point Clouds ◽  
Object Feature ◽  
3D Object Detection ◽  
Feature Alignment

Object detection in 3D point clouds is still a challenging task in autonomous driving. Due to the inherent occlusion and density changes of the point cloud, the data distribution of the same object will change dramatically. Especially, the incomplete data with sparsity or occlusion can not represent the complete characteristics of the object. In this paper, we proposed a novel strong–weak feature alignment algorithm between complete and incomplete objects for 3D object detection, which explores the correlations within the data. It is an end-to-end adaptive network that does not require additional data and can be easily applied to other object detection networks. Through a complete object feature extractor, we achieve a robust feature representation of the object. It serves as a guarding feature to help the incomplete object feature generator to generate effective features. The strong–weak feature alignment algorithm reduces the gap between different states of the same object and enhances the ability to represent the incomplete object. The proposed adaptation framework is validated on the KITTI object benchmark and gets about 6% improvement in detection average precision on 3D moderate difficulty compared to the basic model. The results show that our adaptation method improves the detection performance of incomplete 3D objects.

Sign in / Sign up

Export Citation Format

Share Document