Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients

2016 ◽  
Author(s):  
Zhile Ren ◽  
Erik B. Sudderth
Keyword(s):  
Object Detection ◽  
Three Dimensional ◽  
Dimensional Object

Theory and practice: A two-channel automotive radar for three-dimensional object detection

2015 ◽  
Author(s):  
Xueru Ding ◽  
Aret Carlsen ◽  
Jeff Schaefer ◽  
Matthew Marple ◽  
Dirk Klotzbucher ◽  
...  
Keyword(s):  
Object Detection ◽  
Three Dimensional ◽  
Theory And Practice ◽  
Automotive Radar ◽  
Dimensional Object

Three-Dimensional Object Detection Based on Deep Learning in Enclosed Scenario

2021 ◽  
Author(s):  
Jieming Cui ◽  
Guizhen Yu ◽  
Na Zhang ◽  
Zhangyu Wang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Three Dimensional ◽  
Dimensional Object

Three-Dimensional Object Detection with Deep Neural Networks for Automatic As-Built Reconstruction

2021 ◽  
Vol 147 (9) ◽  
pp. 04021098
Author(s):  
Yongzhi Xu ◽  
Xuesong Shen ◽  
Samsung Lim ◽  
Xuesong Li
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Deep Neural Networks ◽  
Three Dimensional ◽  
Dimensional Object

scholarly journals Real-Time, Three-Dimensional Object Detection and Modeling in Construction

2005 ◽  
Author(s):  
Jochen Teizer ◽  
Frederic Bosche ◽  
Carlos H. Caldas ◽  
Carl T. Haas ◽  
Katherine A. Liapi
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Three Dimensional ◽  
Dimensional Object

scholarly journals Unsupervised Subcategory Domain Adaptive Network for 3D Object Detection in LiDAR

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 927
Author(s):  
Zhiyu Wang ◽  
Li Wang ◽  
Liang Xiao ◽  
Bin Dai
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Three Dimensional ◽  
Autonomous Driving ◽  
Adaptive Method ◽  
Target Domain ◽  
Adaptive Network ◽  
General Object ◽  
Dimensional Object ◽  
3D Object Detection

Three-dimensional object detection based on the LiDAR point cloud plays an important role in autonomous driving. The point cloud distribution of the object varies greatly at different distances, observation angles, and occlusion levels. Besides, different types of LiDARs have different settings of projection angles, thus producing an entirely different point cloud distribution. Pre-trained models on the dataset with annotations may degrade on other datasets. In this paper, we propose a method for object detection using an unsupervised adaptive network, which does not require additional annotation data of the target domain. Our object detection adaptive network consists of a general object detection network, a global feature adaptation network, and a special subcategory instance adaptation network. We divide the source domain data into different subcategories and use a multi-label discriminator to assign labels dynamically to the target domain data. We evaluated our approach on the KITTI object benchmark and proved that the proposed unsupervised adaptive method could achieve a remarkable improvement in the adaptation capabilities.

scholarly journals A novel three-dimensional object detection with the modified You Only Look Once method

2018 ◽  
Vol 15 (2) ◽  
pp. 172988141876550 ◽  
Author(s):  
Xia Zhao ◽  
Haihang Jia ◽  
Yingting Ni
Keyword(s):  
Object Detection ◽  
Three Dimensional ◽  
Dimensional Object

scholarly journals EPGNet: Enhanced Point Cloud Generation for 3D Object Detection

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6927
Author(s):  
Qingsheng Chen ◽  
Cien Fan ◽  
Weizheng Jin ◽  
Lian Zou ◽  
Fangyu Li ◽  
...  
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Three Dimensional ◽  
Autonomous Driving ◽  
Cloud Data ◽  
Second Stage ◽  
Real Scene ◽  
Previous State ◽  
Dimensional Object ◽  
3D Object Detection

Three-dimensional object detection from point cloud data is becoming more and more significant, especially for autonomous driving applications. However, it is difficult for lidar to obtain the complete structure of an object in a real scene due to its scanning characteristics. Although the existing methods have made great progress, most of them ignore the prior information of object structure, such as symmetry. So, in this paper, we use the symmetry of the object to complete the missing part in the point cloud and then detect it. Specifically, we propose a two-stage detection framework. In the first stage, we adopt an encoder–decoder structure to generate the symmetry points of the foreground points and make the symmetry points and the non-empty voxel centers form an enhanced point cloud. In the second stage, the enhanced point cloud is input into the baseline, which is an anchor-based region proposal network, to generate the detection results. Extensive experiments on the challenging KITTI benchmark show the effectiveness of our method, which has better performance on both 3D and BEV (bird’s eye view) object detection compared with some previous state-of-the-art methods.

Development of an artificial compound eye system for three-dimensional object detection

2014 ◽  
Vol 53 (6) ◽  
pp. 1166 ◽  
Author(s):  
Mengchao Ma ◽  
Fang Guo ◽  
Zhaolou Cao ◽  
Keyi Wang
Keyword(s):  
Object Detection ◽  
Compound Eye ◽  
Three Dimensional ◽  
Dimensional Object
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