Semantic Segmentation of 3-D SAR Point Clouds by Graph Method Based on PointNet

Urban object segmentation and classification tasks are critical data processing steps in scene understanding, intelligent vehicles and 3D high-precision maps. Semantic segmentation of 3D point clouds is the foundational step in object recognition. To identify the intersecting objects and improve the accuracy of classification, this paper proposes a segment-based classification method for 3D point clouds. This method firstly divides points into multi-scale supervoxels and groups them by proposed inverse node graph (IN-Graph) construction, which does not need to define prior information about the node, it divides supervoxels by judging the connection state of edges between them. This method reaches minimum global energy by graph cutting, obtains the structural segments as completely as possible, and retains boundaries at the same time. Then, the random forest classifier is utilized for supervised classification. To deal with the mislabeling of scattered fragments, higher-order CRF with small-label cluster optimization is proposed to refine the classification results. Experiments were carried out on mobile laser scan (MLS) point dataset and terrestrial laser scan (TLS) points dataset, and the results show that overall accuracies of 97.57% and 96.39% were obtained in the two datasets. The boundaries of objects were retained well, and the method achieved a good result in the classification of cars and motorcycles. More experimental analyses have verified the advantages of the proposed method and proved the practicability and versatility of the method.

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BushNet: Effective semantic segmentation of bush in large-scale point clouds

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2021.106653 ◽

2022 ◽

Vol 193 ◽

pp. 106653

Author(s):

Hejun Wei ◽

Enyong Xu ◽

Jinlai Zhang ◽

Yanmei Meng ◽

Jin Wei ◽

...

Keyword(s):

Large Scale ◽

Semantic Segmentation ◽

Point Clouds ◽

Scale Point

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Semantic segmentation of dense point clouds

Laser Scanning ◽

10.1201/9781351018869-7 ◽

2019 ◽

pp. 93-111

Author(s):

Martin Weinmann

Keyword(s):

Semantic Segmentation ◽

Point Clouds ◽

Dense Point

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Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds

2018 International Conference on 3D Vision (3DV) ◽

10.1109/3dv.2018.00053 ◽

2018 ◽

Cited By ~ 15

Author(s):

Chris Zhang ◽

Wenjie Luo ◽

Raquel Urtasun

Keyword(s):

Real Time ◽

Semantic Segmentation ◽

Point Clouds ◽

3D Point Clouds

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AN IMPROVED AUTOMATIC POINTWISE SEMANTIC SEGMENTATION OF A 3D URBAN SCENE FROM MOBILE TERRESTRIAL AND AIRBORNE LIDAR POINT CLOUDS: A MACHINE LEARNING APPROACH

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w8-139-2019 ◽

2019 ◽

Vol IV-4/W8 ◽

pp. 139-146

Author(s):

X.-F. Xing ◽

M. A. Mostafavi ◽

G. Edwards ◽

N. Sabo

Keyword(s):

Machine Learning ◽

Urban Areas ◽

Learning Algorithm ◽

Semantic Segmentation ◽

Point Clouds ◽

Airborne Lidar ◽

Urban Scenes ◽

Machine Learning Approach ◽

Urban Scene ◽

Point Level

Abstract. Automatic semantic segmentation of point clouds observed in a 3D complex urban scene is a challenging issue. Semantic segmentation of urban scenes based on machine learning algorithm requires appropriate features to distinguish objects from mobile terrestrial and airborne LiDAR point clouds in point level. In this paper, we propose a pointwise semantic segmentation method based on our proposed features derived from Difference of Normal and the features “directional height above” that compare height difference between a given point and neighbors in eight directions in addition to the features based on normal estimation. Random forest classifier is chosen to classify points in mobile terrestrial and airborne LiDAR point clouds. The results obtained from our experiments show that the proposed features are effective for semantic segmentation of mobile terrestrial and airborne LiDAR point clouds, especially for vegetation, building and ground classes in an airborne LiDAR point clouds in urban areas.

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EXPLORING ALS AND DIM DATA FOR SEMANTIC SEGMENTATION USING CNNS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-347-2018 ◽

2018 ◽

Vol XLII-1 ◽

pp. 347-354 ◽

Cited By ~ 5

Author(s):

F. Politz ◽

M. Sester

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Semantic Segmentation ◽

Point Clouds ◽

Good Alternative ◽

Aerial Images ◽

Learning Approaches ◽

Advantages And Disadvantages ◽

Sensing Applications ◽

High Level

Abstract. Over the past years, the algorithms for dense image matching (DIM) to obtain point clouds from aerial images improved significantly. Consequently, DIM point clouds are now a good alternative to the established Airborne Laser Scanning (ALS) point clouds for remote sensing applications. In order to derive high-level applications such as digital terrain models or city models, each point within a point cloud must be assigned a class label. Usually, ALS and DIM are labelled with different classifiers due to their varying characteristics. In this work, we explore both point cloud types in a fully convolutional encoder-decoder network, which learns to classify ALS as well as DIM point clouds. As input, we project the point clouds onto a 2D image raster plane and calculate the minimal, average and maximal height values for each raster cell. The network then differentiates between the classes ground, non-ground, building and no data. We test our network in six training setups using only one point cloud type, both point clouds as well as several transfer-learning approaches. We quantitatively and qualitatively compare all results and discuss the advantages and disadvantages of all setups. The best network achieves an overall accuracy of 96% in an ALS and 83% in a DIM test set.

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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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HYBRID ACQUISITION OF HIGH QUALITY TRAINING DATA FOR SEMANTIC SEGMENTATION OF 3D POINT CLOUDS USING CROWD-BASED ACTIVE LEARNING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-501-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 501-508

Author(s):

M. Kölle ◽

V. Walter ◽

S. Schmohl ◽

U. Soergel

Keyword(s):

Active Learning ◽

Semantic Segmentation ◽

Point Clouds ◽

Human Interaction ◽

Training Data ◽

Semantic Interpretation ◽

Training Dataset ◽

3D Point Clouds ◽

Semantic Labeling ◽

3D Cnn

Abstract. Automated semantic interpretation of 3D point clouds is crucial for many tasks in the domain of geospatial data analysis. For this purpose, labeled training data is required, which has often to be provided manually by experts. One approach to minimize effort in terms of costs of human interaction is Active Learning (AL). The aim is to process only the subset of an unlabeled dataset that is particularly helpful with respect to class separation. Here a machine identifies informative instances which are then labeled by humans, thereby increasing the performance of the machine. In order to completely avoid involvement of an expert, this time-consuming annotation can be resolved via crowdsourcing. Therefore, we propose an approach combining AL with paid crowdsourcing. Although incorporating human interaction, our method can run fully automatically, so that only an unlabeled dataset and a fixed financial budget for the payment of the crowdworkers need to be provided. We conduct multiple iteration steps of the AL process on the ISPRS Vaihingen 3D Semantic Labeling benchmark dataset (V3D) and especially evaluate the performance of the crowd when labeling 3D points. We prove our concept by using labels derived from our crowd-based AL method for classifying the test dataset. The analysis outlines that by labeling only 0:4% of the training dataset by the crowd and spending less than 145 $, both our trained Random Forest and sparse 3D CNN classifier differ in Overall Accuracy by less than 3 percentage points compared to the same classifiers trained on the complete V3D training set.

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UNDERSTANDING 3D POINT CLOUD DEEP NEURAL NETWORKS BY VISUALIZATION TECHNIQUES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2020-651-2020 ◽

2020 ◽

Vol XLIII-B2-2020 ◽

pp. 651-657

Author(s):

Y. Cao ◽

M. Previtali ◽

M. Scaioni

Keyword(s):

Point Cloud ◽

Semantic Segmentation ◽

Point Clouds ◽

Learning Networks ◽

Quantitative Investigation ◽

Different Types ◽

Visualization Techniques ◽

Point Cloud Classification ◽

Learned Features ◽

Excellent Tool

Abstract. In the wake of the success of Deep Learning Networks (DLN) for image recognition, object detection, shape classification and semantic segmentation, this approach has proven to be both a major breakthrough and an excellent tool in point cloud classification. However, understanding how different types of DLN achieve still lacks. In several studies the output of segmentation/classification process is compared against benchmarks, but the network is treated as a “black-box” and intermediate steps are not deeply analysed. Specifically, here the following questions are discussed: (1) what exactly did DLN learn from a point cloud? (2) On the basis of what information do DLN make decisions? To conduct such a quantitative investigation of these DLN applied to point clouds, this paper investigates the visual interpretability for the decision-making process. Firstly, we introduce a reconstruction network able to reconstruct and visualise the learned features, in order to face with question (1). Then, we propose 3DCAM to indicate the discriminative point cloud regions used by these networks to identify that category, thus dealing with question (2). Through answering the above two questions, the paper would like to offer some initial solutions to better understand the application of DLN to point clouds.

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