scholarly journals Interest point detection from multi‐beam light detection and ranging point cloud using unsupervised convolutional neural network

2020 ◽  
Author(s):  
Deyu Yin ◽  
Qian Zhang ◽  
Jingbin Liu ◽  
Xinlian Liang ◽  
Yunsheng Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Light Detection And Ranging ◽  
Interest Point ◽  
Light Detection ◽  
Interest Point Detection ◽  
Point Detection

scholarly journals Convolutional neural network-based coarse initial position estimation of a monocular camera in large-scale 3D light detection and ranging maps

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141989351
Author(s):  
Manhui Sun ◽  
Shaowu Yang ◽  
Hengzhu Liu
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Convolutional Neural Network ◽  
Initial Position ◽  
Position Estimation ◽  
Light Detection And Ranging ◽  
Depth Image ◽  
Depth Information ◽  
Light Detection ◽  
Monocular Camera

Initial position estimation in global maps, which is a prerequisite for accurate localization, plays a critical role in mobile robot navigation tasks. Global positioning system signals often become unreliable in disaster sites or indoor areas, which require other localization methods to help the robot in searching and rescuing. Many visual-based approaches focus on estimating a robot’s position within prior maps acquired with cameras. In contrast to conventional methods that need a coarse estimation of initial position to precisely localize a camera in a given map, we propose a novel approach that estimates the initial position of a monocular camera within a given 3D light detection and ranging map using a convolutional neural network with no retraining is required. It enables a mobile robot to estimate a coarse position of itself in 3D maps with only a monocular camera. The key idea of our work is to use depth information as intermediate data to retrieve a camera image in immense point clouds. We employ an unsupervised learning framework to predict the depth from a single image. Then we use a pretrained convolutional neural network model to generate depth image descriptors to construct representations of the places. We retrieve the position by computing similarity scores between the current depth image and the depth images projected from the 3D maps. Experiments on the publicly available KITTI data sets have demonstrated the efficiency and feasibility of the presented algorithm.

Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhiyong Gao ◽  
Jianhong Xiang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
3D Object ◽  
3D Object Detection ◽  
Instance Segmentation

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

scholarly journals AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES

2016 ◽  
Vol XLI-B2 ◽  
pp. 35-42 ◽  
Author(s):  
A. W. Lyda ◽  
X. Zhang ◽  
C. L. Glennie ◽  
K. Hudnut ◽  
B. A. Brooks
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Near Field ◽  
Three Dimensional ◽  
Light Detection And Ranging ◽  
Data Sets ◽  
Light Detection ◽  
Icp Algorithm ◽  
Detection Algorithms ◽  
Change Detection Algorithms

Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.

Interest Points Guided Mesh Simplification

2012 ◽  
Vol 263-266 ◽  
pp. 2320-2323 ◽  
Author(s):  
Ying Gao ◽  
Rui Zhao Wang ◽  
Jue Yuan
Keyword(s):  
Mesh Simplification ◽  
Experimental Results ◽  
Interest Point ◽  
Interest Points ◽  
Interest Point Detection ◽  
Feature Preserving ◽  
Error Metric ◽  
Point Detection ◽  
Quadric Error Metric

Based on interest point detection, a feature preserving mesh simplification algorithm is proposed. The Harris operator values of all vertices in the mesh were computed firstly. On the base of Garland’s simplification algorithm, we combine the Harris operator value with quadric error metric and change the order of edge collapsing in the simplification. The experimental results show that the proposed algorithm is effective and feature preserving.

Ship Classification Method for Point Cloud Images Based on Three-Dimensional Convolutional Neural Network

2020 ◽  
Vol 57 (16) ◽  
pp. 161022
Author(s):  
任永梅 Ren Yongmei ◽  
杨杰 Yang Jie ◽  
郭志强 Guo Zhiqiang ◽  
陈奕蕾 Chen Yilei
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Three Dimensional ◽  
Classification Method ◽  
Ship Classification

Point Cloud Registration Based on Convolutional Neural Network

2017 ◽  
Vol 54 (3) ◽  
pp. 031001 ◽  
Author(s):  
舒程珣 Shu Chengxun ◽  
何云涛 He Yuntao ◽  
孙庆科 Sun Qingke
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Point Cloud Registration

scholarly journals Spine Localization in X-ray Images Using Interest Point Detection

2008 ◽  
Vol 22 (3) ◽  
pp. 309-318 ◽  
Author(s):  
Mohammed Benjelloun ◽  
Saïd Mahmoudi
Keyword(s):  
Interest Point ◽  
X Ray ◽  
Interest Point Detection ◽  
Point Detection
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