scholarly journals Ground-distance segmentation of 3D LiDAR point cloud toward autonomous driving

2020 ◽  
Vol 9 ◽  
Author(s):  
Jian Wu ◽  
Qingxiong Yang
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Ground Plane ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Cloud Data ◽  
Dense Point ◽  
3D Lidar

In this paper, we study the semantic segmentation of 3D LiDAR point cloud data in urban environments for autonomous driving, and a method utilizing the surface information of the ground plane was proposed. In practice, the resolution of a LiDAR sensor installed in a self-driving vehicle is relatively low and thus the acquired point cloud is indeed quite sparse. While recent work on dense point cloud segmentation has achieved promising results, the performance is relatively low when directly applied to sparse point clouds. This paper is focusing on semantic segmentation of the sparse point clouds obtained from 32-channel LiDAR sensor with deep neural networks. The main contribution is the integration of the ground information which is used to group ground points far away from each other. Qualitative and quantitative experiments on two large-scale point cloud datasets show that the proposed method outperforms the current state-of-the-art.

scholarly journals Semantic Segmentation of Large-Scale Outdoor Point Clouds by Encoder–Decoder Shared MLPs with Multiple Losses

2021 ◽  
Vol 13 (16) ◽  
pp. 3121
Author(s):  
Beanbonyka Rim ◽  
Ahyoung Lee ◽  
Min Hong
Keyword(s):  
Large Scale ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Trade Off ◽  
Efficiency And Effectiveness ◽  
Benchmark Datasets ◽  
Scale Characteristics ◽  
3D Lidar ◽  
Geometry Mapping

Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand the scene environment in various applications, such as geometry mapping, autonomous driving, and more. With an advantage of being a 3D metric space, 3D LiDAR point clouds, on the other hand, pose a challenge for a deep learning approach, due to their unstructured, unorder, irregular, and large-scale characteristics. Therefore, this paper presents an encoder–decoder shared multi-layer perceptron (MLP) with multiple losses, to address an issue of this semantic segmentation. The challenge rises a trade-off between efficiency and effectiveness in performance. To balance this trade-off, we proposed common mechanisms, which is simple and yet effective, by defining a random point sampling layer, an attention-based pooling layer, and a summation of multiple losses integrated with the encoder–decoder shared MLPs method for the large-scale outdoor point clouds semantic segmentation. We conducted our experiments on the following two large-scale benchmark datasets: Toronto-3D and DALES dataset. Our experimental results achieved an overall accuracy (OA) and a mean intersection over union (mIoU) of both the Toronto-3D dataset, with 83.60% and 71.03%, and the DALES dataset, with 76.43% and 59.52%, respectively. Additionally, our proposed method performed a few numbers of parameters of the model, and faster than PointNet++ by about three times during inferencing.

scholarly journals Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions

2020 ◽  
Vol 10 (7) ◽  
pp. 2391
Author(s):  
Can Chen ◽  
Luca Zanotti Fragonara ◽  
Antonios Tsourdos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Point Cloud ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Fine Grained ◽  
Point Cloud Analysis ◽  
Cloud Analysis

In order to achieve a better performance for point cloud analysis, many researchers apply deep neural networks using stacked Multi-Layer-Perceptron (MLP) convolutions over an irregular point cloud. However, applying these dense MLP convolutions over a large amount of points (e.g., autonomous driving application) leads to limitations due to the computation and memory capabilities. To achieve higher performances but decrease the computational complexity, we propose a deep-wide neural network, named ShufflePointNet, which can exploit fine-grained local features, but also reduce redundancies using group convolution and channel shuffle operation. Unlike conventional operations that directly apply MLPs on the high-dimensional features of a point cloud, our model goes “wider” by splitting features into groups with smaller depth in advance, having the respective MLP computations applied only to a single group, which can significantly reduce complexity and computation. At the same time, we allow communication between groups by shuffling the feature channel to capture fine-grained features. We further discuss the multi-branch method for wider neural networks being also beneficial to feature extraction for point clouds. We present extensive experiments for shape classification tasks on a ModelNet40 dataset and semantic segmentation task on large scale datasets ShapeNet part, S3DIS and KITTI. Finally, we carry out an ablation study and compare our model to other state-of-the-art algorithms to show its efficiency in terms of complexity and accuracy.

scholarly journals A Deep Learning-Based Perception Algorithm Using 3D LiDAR for Autonomous Driving: Simultaneous Segmentation and Detection Network (SSADNet)

2020 ◽  
Vol 10 (13) ◽  
pp. 4486 ◽  
Author(s):  
Yongbeom Lee ◽  
Seongkeun Park
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Autonomous Driving ◽  
Light Detection And Ranging ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Lidar ◽  
Top View ◽  
Single Neural Network ◽  
Reflection Intensity

In this paper, we propose a deep learning-based perception method in autonomous driving systems using a Light Detection and Ranging(LiDAR) point cloud data, which is called a simultaneous segmentation and detection network (SSADNet). SSADNet can be used to recognize both drivable areas and obstacles, which is necessary for autonomous driving. Unlike the previous methods, where separate networks were needed for segmentation and detection, SSADNet can perform segmentation and detection simultaneously based on a single neural network. The proposed method uses point cloud data obtained from a 3D LiDAR for network input to generate a top view image consisting of three channels of distance, height, and reflection intensity. The structure of the proposed network includes a branch for segmentation and a branch for detection as well as a bridge connecting the two parts. The KITTI dataset, which is often used for experiments on autonomous driving, was used for training. The experimental results show that segmentation and detection can be performed simultaneously for drivable areas and vehicles at a quick inference speed, which is appropriate for autonomous driving systems.

scholarly journals Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods

2019 ◽  
Vol 8 (5) ◽  
pp. 213 ◽  
Author(s):  
Florent Poux ◽  
Roland Billen
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Semantic Representation ◽  
Structural Connectivity ◽  
Three Dimensional ◽  
Strong Support ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Learning Methods ◽  
Cloud Data

Automation in point cloud data processing is central in knowledge discovery within decision-making systems. The definition of relevant features is often key for segmentation and classification, with automated workflows presenting the main challenges. In this paper, we propose a voxel-based feature engineering that better characterize point clusters and provide strong support to supervised or unsupervised classification. We provide different feature generalization levels to permit interoperable frameworks. First, we recommend a shape-based feature set (SF1) that only leverages the raw X, Y, Z attributes of any point cloud. Afterwards, we derive relationship and topology between voxel entities to obtain a three-dimensional (3D) structural connectivity feature set (SF2). Finally, we provide a knowledge-based decision tree to permit infrastructure-related classification. We study SF1/SF2 synergy on a new semantic segmentation framework for the constitution of a higher semantic representation of point clouds in relevant clusters. Finally, we benchmark the approach against novel and best-performing deep-learning methods while using the full S3DIS dataset. We highlight good performances, easy-integration, and high F1-score (> 85%) for planar-dominant classes that are comparable to state-of-the-art deep learning.

scholarly journals A REGISTRATION METHOD OF POINT CLOUDS COLLECTED BY MOBILE LIDAR USING SOLELY STANDARD LAS FILES INFORMATION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 121-128 ◽  
Author(s):  
L. Gézero ◽  
C. Antunes
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Auxiliary Information ◽  
Point Clouds ◽  
Registration Method ◽  
Digital Terrain ◽  
Terrain Models ◽  
Dense Point ◽  
Fully Automatic ◽  
Method Point

In the last few years, LiDAR sensors installed in terrestrial vehicles have been revealed as an efficient method to collect very dense 3D georeferenced information. The possibility of creating very dense point clouds representing the surface surrounding the sensor, at a given moment, in a very fast, detailed and easy way, shows the potential of this technology to be used for cartography and digital terrain models production in large scale. However, there are still some limitations associated with the use of this technology. When several acquisitions of the same area with the same device, are made, differences between the clouds can be observed. The range of that differences can go from few centimetres to some several tens of centimetres, mainly in urban and high vegetation areas where the occultation of the GNSS system introduces a degradation of the georeferenced trajectory. Along this article a different method point cloud registration is proposed. In addition to the efficiency and speed of execution, the main advantages of the method are related to the fact that the adjustment is continuously made over the trajectory, based on the GPS time. The process is fully automatic and only information recorded in the standard LAS files is used, without the need for any auxiliary information, in particular regarding the trajectory.

Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset

2021 ◽  
pp. 027836492110067
Author(s):  
Jens Behley ◽  
Martin Garbade ◽  
Andres Milioto ◽  
Jan Quenzel ◽  
Sven Behnke ◽  
...  
Keyword(s):  
Point Cloud ◽  
Scene Understanding ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Lessons Learned ◽  
Multiple Point ◽  
3D Point Cloud ◽  
Core Capability ◽  
Semantic Scene ◽  
3D Lidar

A holistic semantic scene understanding exploiting all available sensor modalities is a core capability to master self-driving in complex everyday traffic. To this end, we present the SemanticKITTI dataset that provides point-wise semantic annotations of Velodyne HDL-64E point clouds of the KITTI Odometry Benchmark. Together with the data, we also published three benchmark tasks for semantic scene understanding covering different aspects of semantic scene understanding: (1) semantic segmentation for point-wise classification using single or multiple point clouds as input; (2) semantic scene completion for predictive reasoning on the semantics and occluded regions; and (3) panoptic segmentation combining point-wise classification and assigning individual instance identities to separate objects of the same class. In this article, we provide details on our dataset showing an unprecedented number of fully annotated point cloud sequences, more information on our labeling process to efficiently annotate such a vast amount of point clouds, and lessons learned in this process. The dataset and resources are available at http://www.semantic-kitti.org .

scholarly journals JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds

2020 ◽  
Vol 34 (07) ◽  
pp. 12951-12958 ◽  
Author(s):  
Lin Zhao ◽  
Wenbing Tao
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Feature Fusion ◽  
Mean Shift ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Semantic Features ◽  
Backbone Network ◽  
3D Point Clouds ◽  
Instance Segmentation

In this paper, we propose a novel joint instance and semantic segmentation approach, which is called JSNet, in order to address the instance and semantic segmentation of 3D point clouds simultaneously. Firstly, we build an effective backbone network to extract robust features from the raw point clouds. Secondly, to obtain more discriminative features, a point cloud feature fusion module is proposed to fuse the different layer features of the backbone network. Furthermore, a joint instance semantic segmentation module is developed to transform semantic features into instance embedding space, and then the transformed features are further fused with instance features to facilitate instance segmentation. Meanwhile, this module also aggregates instance features into semantic feature space to promote semantic segmentation. Finally, the instance predictions are generated by applying a simple mean-shift clustering on instance embeddings. As a result, we evaluate the proposed JSNet on a large-scale 3D indoor point cloud dataset S3DIS and a part dataset ShapeNet, and compare it with existing approaches. Experimental results demonstrate our approach outperforms the state-of-the-art method in 3D instance segmentation with a significant improvement in 3D semantic prediction and our method is also beneficial for part segmentation. The source code for this work is available at https://github.com/dlinzhao/JSNet.

scholarly journals Efficient Calculation Method for Tree Stem Traits from Large-Scale Point Clouds of Forest Stands

2021 ◽  
Vol 13 (13) ◽  
pp. 2476
Author(s):  
Hiroshi Masuda ◽  
Yuichiro Hiraoka ◽  
Kazuto Saito ◽  
Shinsuke Eto ◽  
Michinari Matsushita ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Random Access ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Forest Stands ◽  
Cross Sectional ◽  
Cloud Data ◽  
Wireframe Model

With the use of terrestrial laser scanning (TLS) in forest stands, surveys are now equipped to obtain dense point cloud data. However, the data range, i.e., the number of points, often reaches the billions or even higher, exceeding random access memory (RAM) limits on common computers. Moreover, the processing time often also extends beyond acceptable processing lengths. Thus, in this paper, we present a new method of efficiently extracting stem traits from huge point cloud data obtained by TLS, without subdividing or downsampling the point clouds. In this method, each point cloud is converted into a wireframe model by connecting neighboring points on the same continuous surface, and three-dimensional points on stems are resampled as cross-sectional points of the wireframe model in an out-of-core manner. Since the data size of the section points is much smaller than the original point clouds, stem traits can be calculated from the section points on a common computer. With the study method, 1381 tree stems were calculated from 3.6 billion points in ~20 min on a common computer. To evaluate the accuracy of this method, eight targeted trees were cut down and sliced at 1-m intervals; actual stem traits were then compared to those calculated from point clouds. The experimental results showed that the efficiency and accuracy of the proposed method are sufficient for practical use in various fields, including forest management and forest research.

scholarly journals Panicle-3D: Efficient Phenotyping Tool for Precise Semantic Segmentation of Rice Panicle Point Cloud

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liang Gong ◽  
Xiaofeng Du ◽  
Kai Zhu ◽  
Ke Lin ◽  
Qiaojun Lou ◽  
...  
Keyword(s):  
Point Cloud ◽  
Network Models ◽  
Semantic Segmentation ◽  
Spatial Location ◽  
Point Clouds ◽  
Utilization Efficiency ◽  
Data Set ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Segmentation Accuracy

The automated measurement of crop phenotypic parameters is of great significance to the quantitative study of crop growth. The segmentation and classification of crop point cloud help to realize the automation of crop phenotypic parameter measurement. At present, crop spike-shaped point cloud segmentation has problems such as fewer samples, uneven distribution of point clouds, occlusion of stem and spike, disorderly arrangement of point clouds, and lack of targeted network models. The traditional clustering method can realize the segmentation of the plant organ point cloud with relatively independent spatial location, but the accuracy is not acceptable. This paper first builds a desktop-level point cloud scanning apparatus based on a structured-light projection module to facilitate the point cloud acquisition process. Then, the rice ear point cloud was collected, and the rice ear point cloud data set was made. In addition, data argumentation is used to improve sample utilization efficiency and training accuracy. Finally, a 3D point cloud convolutional neural network model called Panicle-3D was designed to achieve better segmentation accuracy. Specifically, the design of Panicle-3D is aimed at the multiscale characteristics of plant organs, combined with the structure of PointConv and long and short jumps, which accelerates the convergence speed of the network and reduces the loss of features in the process of point cloud downsampling. After comparison experiments, the segmentation accuracy of Panicle-3D reaches 93.4%, which is higher than PointNet. Panicle-3D is suitable for other similar crop point cloud segmentation tasks.

scholarly journals Evaluation of Partially Overlapping 3D Point Cloud's Registration by using ICP variant and CloudCompare.

2014 ◽  
Vol XL-8 ◽  
pp. 891-897 ◽  
Author(s):  
Y. D. Rajendra ◽  
S. C. Mehrotra ◽  
K. V. Kale ◽  
R. R. Manza ◽  
R. K. Dhumal ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Large Object ◽  
Point Cloud Registration ◽  
Icp Algorithm ◽  
Cloud Data ◽  
Registration Process ◽  
Heritage Site ◽  
Laser Scanners ◽  
Dense Point

Terrestrial Laser Scanners (TLS) are used to get dense point samples of large object’s surface. TLS is new and efficient method to digitize large object or scene. The collected point samples come into different formats and coordinates. Different scans are required to scan large object such as heritage site. Point cloud registration is considered as important task to bring different scans into whole 3D model in one coordinate system. Point clouds can be registered by using one of the three ways or combination of them, Target based, feature extraction, point cloud based. For the present study we have gone through Point Cloud Based registration approach. We have collected partially overlapped 3D Point Cloud data of Department of Computer Science & IT (DCSIT) building located in Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. To get the complete point cloud information of the building we have taken 12 scans, 4 scans for exterior and 8 scans for interior façade data collection. There are various algorithms available in literature, but Iterative Closest Point (ICP) is most dominant algorithms. The various researchers have developed variants of ICP for better registration process. The ICP point cloud registration algorithm is based on the search of pairs of nearest points in a two adjacent scans and calculates the transformation parameters between them, it provides advantage that no artificial target is required for registration process. We studied and implemented three variants Brute Force, KDTree, Partial Matching of ICP algorithm in MATLAB. The result shows that the implemented version of ICP algorithm with its variants gives better result with speed and accuracy of registration as compared with CloudCompare Open Source software.

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