scholarly journals Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2113
Author(s):  
Can Chen ◽  
Luca Zanotti Fragonara ◽  
Antonios Tsourdos
Keyword(s):  
Object Tracking ◽  
Autonomous Navigation ◽  
Data Association ◽  
Point Clouds ◽  
Feature Interaction ◽  
Affinity Matrix ◽  
Motion Feature ◽  
3D Space ◽  
3D Point Clouds ◽  
Extensive Evaluation

Autonomous systems need to localize and track surrounding objects in 3D space for safe motion planning. As a result, 3D multi-object tracking (MOT) plays a vital role in autonomous navigation. Most MOT methods use a tracking-by-detection pipeline, which includes both the object detection and data association tasks. However, many approaches detect objects in 2D RGB sequences for tracking, which lacks reliability when localizing objects in 3D space. Furthermore, it is still challenging to learn discriminative features for temporally consistent detection in different frames, and the affinity matrix is typically learned from independent object features without considering the feature interaction between detected objects in the different frames. To settle these problems, we first employ a joint feature extractor to fuse the appearance feature and the motion feature captured from 2D RGB images and 3D point clouds, and then we propose a novel convolutional operation, named RelationConv, to better exploit the correlation between each pair of objects in the adjacent frames and learn a deep affinity matrix for further data association. We finally provide extensive evaluation to reveal that our proposed model achieves state-of-the-art performance on the KITTI tracking benchmark.

scholarly journals Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans

2020 ◽  
Vol 10 (3) ◽  
pp. 1140 ◽  
Author(s):  
Jorge L. Martínez ◽  
Mariano Morán ◽  
Jesús Morales ◽  
Alfredo Robles ◽  
Manuel Sánchez
Keyword(s):  
Supervised Learning ◽  
Autonomous Navigation ◽  
Three Dimensional ◽  
Point Clouds ◽  
Natural Environments ◽  
Good Prediction ◽  
Ground Vehicles ◽  
3D Point Clouds ◽  
Natural Terrain ◽  
Robotic Simulator

Autonomous navigation of ground vehicles on natural environments requires looking for traversable terrain continuously. This paper develops traversability classifiers for the three-dimensional (3D) point clouds acquired by the mobile robot Andabata on non-slippery solid ground. To this end, different supervised learning techniques from the Python library Scikit-learn are employed. Training and validation are performed with synthetic 3D laser scans that were labelled point by point automatically with the robotic simulator Gazebo. Good prediction results are obtained for most of the developed classifiers, which have also been tested successfully on real 3D laser scans acquired by Andabata in motion.

scholarly journals GENERATION OF GROUND TRUTH DATASETS FOR THE ANALYSIS OF 3D POINT CLOUDS IN URBAN SCENES ACQUIRED VIA DIFFERENT SENSORS

2018 ◽  
Vol XLII-3 ◽  
pp. 2009-2015 ◽  
Author(s):  
Y. Xu ◽  
Z. Sun ◽  
R. Boerner ◽  
T. Koch ◽  
L. Hoegner ◽  
...  
Keyword(s):  
Point Cloud ◽  
Spatial Information ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Urban Scenes ◽  
3D Space ◽  
3D Point Clouds ◽  
Testing Site ◽  
Voxel Grid

In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line- and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.

scholarly journals Scale-Aware Attention-Based PillarsNet (SAPN) Based 3D Object Detection for Point Cloud

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xiang Song ◽  
Weiqin Zhan ◽  
Xiaoyu Che ◽  
Huilin Jiang ◽  
Biao Yang
Keyword(s):  
Object Detection ◽  
Autonomous Navigation ◽  
Point Clouds ◽  
Detection Performance ◽  
Detection Methods ◽  
Feature Maps ◽  
3D Object ◽  
3D Point Clouds ◽  
Detection Approach ◽  
3D Object Detection

Three-dimensional object detection can provide precise positions of objects, which can be beneficial to many robotics applications, such as self-driving cars, housekeeping robots, and autonomous navigation. In this work, we focus on accurate object detection in 3D point clouds and propose a new detection pipeline called scale-aware attention-based PillarsNet (SAPN). SAPN is a one-stage 3D object detection approach similar to PointPillar. However, SAPN achieves better performance than PointPillar by introducing the following strategies. First, we extract multiresolution pillar-level features from the point clouds to make the detection approach more scale-aware. Second, a spatial-attention mechanism is used to highlight the object activations in the feature maps, which can improve detection performance. Finally, SE-attention is employed to reweight the features fed into the detection head, which performs 3D object detection in a multitask learning manner. Experiments on the KITTI benchmark show that SAPN achieved similar or better performance compared with several state-of-the-art LiDAR-based 3D detection methods. The ablation study reveals the effectiveness of each proposed strategy. Furthermore, strategies used in this work can be embedded easily into other LiDAR-based 3D detection approaches, which improve their detection performance with slight modifications.

scholarly journals Edge Detection in 3D Point Clouds Using Digital Images

2021 ◽  
Vol 10 (4) ◽  
pp. 229
Author(s):  
Maria Melina Dolapsaki ◽  
Andreas Georgopoulos
Keyword(s):  
Point Cloud ◽  
Digital Images ◽  
Point Clouds ◽  
Analytical Geometry ◽  
3D Space ◽  
Automated Method ◽  
3D Point Clouds ◽  
Edge Points ◽  
True Position ◽  
Cultural Heritage Objects

This paper presents an effective and semi-automated method for detecting 3D edges in 3D point clouds with the help of high-resolution digital images. The effort aims to contribute towards addressing the unsolved problem of automated production of vector drawings from 3D point clouds of cultural heritage objects. Edges are the simplest primitives to detect in an unorganized point cloud and an algorithm was developed to perform this task. The provided edges are defined and measured on 2D digital images of known orientation, and the algorithm determines the plane defined by the edge on the image and its perspective center. This is accomplished by applying suitable transformations to the image coordinates of the edge points based on the Analytical Geometry relationships and properties of planes in 3D space. This plane inevitably contains the 3D points of the edge in the point cloud. The algorithm then detects and isolates those points which define the edge in the world system. Finally, the goal is to reliably locate the points that describe the desired edge in their true position in the geodetic space, using several constraints. The algorithm is firstly investigated theoretically for its efficiency using simulation data and then assessed under real conditions and under different image orientations and lengths of the edge on the image. The results are presented and evaluated.

scholarly journals Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1228
Author(s):  
Ting On Chan ◽  
Linyuan Xia ◽  
Yimin Chen ◽  
Wei Lang ◽  
Tingting Chen ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Geometric Model ◽  
Digital Camera ◽  
Point Clouds ◽  
Symmetry Analysis ◽  
3D Point Clouds ◽  
Transmission Towers ◽  
Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

scholarly journals Using Least-Squares Residuals to Assess the Stochasticity of Measurements—Example: Terrestrial Laser Scanner and Surface Modeling

2021 ◽  
Vol 5 (1) ◽  
pp. 59
Author(s):  
Gaël Kermarrec ◽  
Niklas Schild ◽  
Jan Hartmann
Keyword(s):  
Least Squares ◽  
Laser Scanner ◽  
Real Data ◽  
Point Clouds ◽  
Statistical Testing ◽  
Normal Vector ◽  
Engineering Structures ◽  
3D Point Clouds ◽  
Least Squares Adjustment ◽  
Correlation Parameters

Terrestrial laser scanners (TLS) capture a large number of 3D points rapidly, with high precision and spatial resolution. These scanners are used for applications as diverse as modeling architectural or engineering structures, but also high-resolution mapping of terrain. The noise of the observations cannot be assumed to be strictly corresponding to white noise: besides being heteroscedastic, correlations between observations are likely to appear due to the high scanning rate. Unfortunately, if the variance can sometimes be modeled based on physical or empirical considerations, the latter are more often neglected. Trustworthy knowledge is, however, mandatory to avoid the overestimation of the precision of the point cloud and, potentially, the non-detection of deformation between scans recorded at different epochs using statistical testing strategies. The TLS point clouds can be approximated with parametric surfaces, such as planes, using the Gauss–Helmert model, or the newly introduced T-splines surfaces. In both cases, the goal is to minimize the squared distance between the observations and the approximated surfaces in order to estimate parameters, such as normal vector or control points. In this contribution, we will show how the residuals of the surface approximation can be used to derive the correlation structure of the noise of the observations. We will estimate the correlation parameters using the Whittle maximum likelihood and use comparable simulations and real data to validate our methodology. Using the least-squares adjustment as a “filter of the geometry” paves the way for the determination of a correlation model for many sensors recording 3D point clouds.

A method of calculating the leafstalk angle of the soybean canopy based on 3D point clouds

2021 ◽  
Vol 42 (7) ◽  
pp. 2463-2484
Author(s):  
Kexin Zhu ◽  
Xiaodan Ma ◽  
Haiou Guan ◽  
Jiarui Feng ◽  
Zhichao Zhang ◽  
...  
Keyword(s):  
Point Clouds ◽  
3D Point Clouds ◽  
Soybean Canopy

A method for calculating the leaf inclination of soybean canopy based on 3D point clouds

2021 ◽  
Vol 42 (15) ◽  
pp. 5721-5742
Author(s):  
Zhichao Zhang ◽  
Xiaodan Ma ◽  
Haiou Guan ◽  
Kexin Zhu ◽  
Jiarui Feng ◽  
...  
Keyword(s):  
Point Clouds ◽  
3D Point Clouds ◽  
Leaf Inclination ◽  
Soybean Canopy
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