Research on Deep Learning Based Optimal Combination of Multidimensional Features in Large-Scene Laser Point Clouds Classification

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

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A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras

Applied Sciences ◽

10.3390/app11125503 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5503

Author(s):

Munkhjargal Gochoo ◽

Syeda Amna Rizwan ◽

Yazeed Yasin Ghadi ◽

Ahmad Jalal ◽

Kibum Kim

Keyword(s):

Deep Learning ◽

Human Head ◽

Point Clouds ◽

Head Tracking ◽

Complex Environments ◽

People Tracking ◽

Practical Applications ◽

Remote Cameras ◽

Video Frames ◽

Benchmark Datasets

Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases; at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results.

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Deep Learning-Based Classification and Reconstruction of Residential Scenes From Large-Scale Point Clouds

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2017.2769120 ◽

2018 ◽

Vol 56 (4) ◽

pp. 1887-1897 ◽

Cited By ~ 13

Author(s):

Liqiang Zhang ◽

Liang Zhang

Keyword(s):

Deep Learning ◽

Large Scale ◽

Point Clouds ◽

Scale Point

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Parsing of Urban Facades from 3D Point Clouds Based on a Novel Multi-View Domain

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.87.4.283 ◽

2021 ◽

Vol 87 (4) ◽

pp. 283-293

Author(s):

Wei Wang ◽

Yuan Xu ◽

Yingchao Ren ◽

Gang Wang

Keyword(s):

Deep Learning ◽

Prior Knowledge ◽

Performance Improvement ◽

Data Distribution ◽

Point Clouds ◽

Learning Models ◽

Data Set ◽

3D Point Clouds ◽

Segmentation Accuracy ◽

The Mean

Recently, performance improvement in facade parsing from 3D point clouds has been brought about by designing more complex network structures, which cost huge computing resources and do not take full advantage of prior knowledge of facade structure. Instead, from the perspective of data distribution, we construct a new hierarchical mesh multi-view data domain based on the characteristics of facade objects to achieve fusion of deep-learning models and prior knowledge, thereby significantly improving segmentation accuracy. We comprehensively evaluate the current mainstream method on the RueMonge 2014 data set and demonstrate the superiority of our method. The mean intersection-over-union index on the facade-parsing task reached 76.41%, which is 2.75% higher than the current best result. In addition, through comparative experiments, the reasons for the performance improvement of the proposed method are further analyzed.

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Sharpness fields in point clouds using deep learning

Computers & Graphics ◽

10.1016/j.cag.2018.11.003 ◽

2019 ◽

Vol 78 ◽

pp. 37-53 ◽

Cited By ~ 5

Author(s):

Prashant Raina ◽

Sudhir Mudur ◽

Tiberiu Popa

Keyword(s):

Deep Learning ◽

Point Clouds

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Principal Component Analysis For ICP Pose Estimation Of Space Structures

10.32920/ryerson.14656941 ◽

2021 ◽

Author(s):

Lun H. Mark

Keyword(s):

Pose Estimation ◽

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Optimal Combination ◽

Estimation Accuracy ◽

Space Structures ◽

Complex Objects ◽

Error Norm ◽

Simulation Results

This thesis investigates how geometry of complex objects is related to LIDAR scanning with the Iterative Closest Point (ICP) pose estimation and provides statistical means to assess the pose accuracy. LIDAR scanners have become essential parts of space vision systems for autonomous docking and rendezvous. Principal Componenet Analysis based geometric constraint indices have been found to be strongly related to the pose error norm and the error of each individual degree of freedom. This leads to the development of several strategies for identifying the best view of an object and the optimal combination of localized scanned areas of the object's surface to achieve accurate pose estimation. Also investigated is the possible relation between the ICP pose estimation accuracy and the districution or allocation of the point cloud. The simulation results were validated using point clouds generated by scanning models of Quicksat and a cuboctahedron using Neptec's TriDAR scanner.

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Principal Component Analysis For ICP Pose Estimation Of Space Structures

10.32920/ryerson.14656941.v1 ◽

2021 ◽

Author(s):

Lun H. Mark

Keyword(s):

Pose Estimation ◽

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Optimal Combination ◽

Estimation Accuracy ◽

Space Structures ◽

Complex Objects ◽

Error Norm ◽

Simulation Results

This thesis investigates how geometry of complex objects is related to LIDAR scanning with the Iterative Closest Point (ICP) pose estimation and provides statistical means to assess the pose accuracy. LIDAR scanners have become essential parts of space vision systems for autonomous docking and rendezvous. Principal Componenet Analysis based geometric constraint indices have been found to be strongly related to the pose error norm and the error of each individual degree of freedom. This leads to the development of several strategies for identifying the best view of an object and the optimal combination of localized scanned areas of the object's surface to achieve accurate pose estimation. Also investigated is the possible relation between the ICP pose estimation accuracy and the districution or allocation of the point cloud. The simulation results were validated using point clouds generated by scanning models of Quicksat and a cuboctahedron using Neptec's TriDAR scanner.

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3DLEB-Net: Label-Efficient Deep Learning-Based Semantic Segmentation of Building Point Clouds at LoD3 Level

Applied Sciences ◽

10.3390/app11198996 ◽

2021 ◽

Vol 11 (19) ◽

pp. 8996

Author(s):

Yuwei Cao ◽

Marco Scaioni

Keyword(s):

Deep Learning ◽

Semantic Segmentation ◽

Point Clouds ◽

Training Data ◽

Second Step ◽

Point Cloud Data ◽

Dynamic Graph ◽

Cloud Data ◽

Supervised Methods ◽

Global And Local

In current research, fully supervised Deep Learning (DL) techniques are employed to train a segmentation network to be applied to point clouds of buildings. However, training such networks requires large amounts of fine-labeled buildings’ point-cloud data, presenting a major challenge in practice because they are difficult to obtain. Consequently, the application of fully supervised DL for semantic segmentation of buildings’ point clouds at LoD3 level is severely limited. In order to reduce the number of required annotated labels, we proposed a novel label-efficient DL network that obtains per-point semantic labels of LoD3 buildings’ point clouds with limited supervision, named 3DLEB-Net. In general, it consists of two steps. The first step (Autoencoder, AE) is composed of a Dynamic Graph Convolutional Neural Network (DGCNN) encoder and a folding-based decoder. It is designed to extract discriminative global and local features from input point clouds by faithfully reconstructing them without any label. The second step is the semantic segmentation network. By supplying a small amount of task-specific supervision, a segmentation network is proposed for semantically segmenting the encoded features acquired from the pre-trained AE. Experimentally, we evaluated our approach based on the Architectural Cultural Heritage (ArCH) dataset. Compared to the fully supervised DL methods, we found that our model achieved state-of-the-art results on the unseen scenes, with only 10% of labeled training data from fully supervised methods as input. Moreover, we conducted a series of ablation studies to show the effectiveness of the design choices of our model.

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Classification of Point Clouds for Indoor Components Using Few Labeled Samples

Remote Sensing ◽

10.3390/rs12142181 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2181

Author(s):

Hangbin Wu ◽

Huimin Yang ◽

Shengyu Huang ◽

Doudou Zeng ◽

Chun Liu ◽

...

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Point Clouds ◽

Neighborhood Search ◽

Learning Methods ◽

Semantic Classification ◽

Cloud Classification ◽

Mixed Features ◽

Indoor Scenarios ◽

Point Cloud Classification

The existing deep learning methods for point cloud classification are trained using abundant labeled samples and used to test only a few samples. However, classification tasks are diverse, and not all tasks have enough labeled samples for training. In this paper, a novel point cloud classification method for indoor components using few labeled samples is proposed to solve the problem of the requirement for abundant labeled samples for training with deep learning classification methods. This method is composed of four parts: mixing samples, feature extraction, dimensionality reduction, and semantic classification. First, the few labeled point clouds are mixed with unlabeled point clouds. Next, the mixed high-dimensional features are extracted using a deep learning framework. Subsequently, a nonlinear manifold learning method is used to embed the mixed features into a low-dimensional space. Finally, the few labeled point clouds in each cluster are identified, and semantic labels are provided for unlabeled point clouds in the same cluster by a neighborhood search strategy. The validity and versatility of the proposed method were validated by different experiments and compared with three state-of-the-art deep learning methods. Our method uses fewer than 30 labeled point clouds to achieve an accuracy that is 1.89–19.67% greater than existing methods. More importantly, the experimental results suggest that this method is not only suitable for single-attribute indoor scenarios but also for comprehensive complex indoor scenarios.

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