A Review of Deep Learning-Based Semantic Segmentation for Point Cloud

Zhang, Jiaying; Zhao, Xiaoli; Chen, Zheng; Lu, Zhejun

doi:10.1109/access.2019.2958671

Cited by 206 publications

(105 citation statements)

References 76 publications

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“…According to the literature, semantic segmentation methods for 3D point cloud can be divided into two groups: a) projection-based methods and b) point-based methods (Zhang, Zhao, Chen, & Lu, 2019), which are going to be described in the following.…”

Section: Deep Learningmentioning

confidence: 99%

Towards an Integrated Design Methodology for H-Bim

Pellis

Masiero

Tucci

et al. 2021

Proceedings ARQUEOLÓGICA 2.0 - 9th International Congress &Amp; 3rd GEORES - GEOmatics and pREServation

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In recent years, the numerous advantages introduced by Building Information modelling (BIM) have led in its application on the heritage environment and giving birth to the concept of H-BIM (Heritage BIM). The resulting demand in heritage survey data processing has focused this research on the development of strategies and methods to improve the construction of three-dimensional and informative models starting from 3D point clouds. The implementation of an automated procedure is fundamental for easing and speeding up the survey data processing and one of the most challenging tasks to achieve this purpose is the problem of semantic segmentation. The research presented in this paper aims at testing already existing methods and exploring new strategies for 3D point cloud semantic segmentation on heritage scenarios focusing on deep learning and neural network techniques.

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Section: Deep Learningmentioning

confidence: 99%

Towards an Integrated Design Methodology for H-Bim

Pellis

Masiero

Tucci

et al. 2021

Proceedings ARQUEOLÓGICA 2.0 - 9th International Congress &Amp; 3rd GEORES - GEOmatics and pREServation

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“…2021, 13, x FOR PEER REVIEW 6 of 21 façade planes to remove the corresponding points and break the continuity between objects; then, the objects are individualised with connected components [26]. Other options are to structure the cloud into super voxels [31], to cluster points with superpoint graphs [32] or to implement techniques based on Deep Learning [33]. The ten selected classes were: bench, car, lamppost, motorbike, pedestrian, traffic light, traffic sign, tree, wastebasket, and waste container.…”

Section: Dataset and Pre-trained Cnnmentioning

confidence: 99%

Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

et al. 2021

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Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

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“…Deep learning is widely used in various fields, such as natural language processing [21], speech recognition [17], image processing [15], and point cloud processing [3,12,20,48,51], and others. Goodfellow et al [11].…”

Section: Related Study 21 3d Deep Learningmentioning

confidence: 99%

Semantic Segmentation for Full-Waveform LiDAR Data Using Local and Hierarchical Global Feature Extraction

Shinohara

Xiu

Matsuoka

2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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During the last few years, in the field of computer vision, sophisticated deep learning methods have been developed to accomplish semantic segmentation tasks of 3D point cloud data. Additionally, many researchers have extended the applicability of these methods, such as PointNet or PointNet++, beyond semantic segmentation tasks of indoor scene data to large-scale outdoor scene data observed using airborne laser scanning systems equipped with light detection and ranging (LiDAR) technology. Most extant studies have only investigated geometric information (x, y, and z or longitude, latitude, and height) and have omitted rich radiometric information. Therefore, we aim to extend the applicability of deep learning-based model from the geometric data into radiometric data acquired with airborne full-waveform LiDAR without converting the waveform into 2D images or 3D voxels. We simultaneously train two models: a local module for local feature extraction and a global module for acquiring wide receptive fields for the waveform. Furthermore, our proposed model is based on waveform-aware convolutional techniques. We evaluate the effectiveness of the proposed method using benchmark large-scale outdoor scene data. By integrating the two outputs from the local module and the global module, our proposed model had achieved higher mean recall value 0.92 than previous methods and higher F1 scores for all six classes than the other 3D Deep Learning method. Therefore, our proposed network consisting of the local and global module successfully resolves the semantic segmentation task of full-waveform LiDAR data without requiring expert knowledge. CCS CONCEPTS • Computing methodologies → Scene understanding.

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A Review of Deep Learning-Based Semantic Segmentation for Point Cloud

Cited by 206 publications

References 76 publications

Towards an Integrated Design Methodology for H-Bim

Towards an Integrated Design Methodology for H-Bim

Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

Semantic Segmentation for Full-Waveform LiDAR Data Using Local and Hierarchical Global Feature Extraction

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