scholarly journals Deep Learning Methods Applied to 3D Point Clouds Based Instance Segmentation: A Review

2021 ◽  
Author(s):  
Desire Mulindwa Burume ◽  
Shengzhi Du
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Indoor Navigation ◽  
Future Directions ◽  
3D Point Clouds ◽  
Speed Accuracy ◽  
Learning Architectures ◽  
Instance Segmentation

Beyond semantic segmentation,3D instance segmentation(a process to delineate objects of interest and also classifying the objects into a set of categories) is gaining more and more interest among researchers since numerous computer vision applications need accurate segmentation processes(autonomous driving, indoor navigation, and even virtual or augmented reality systems…) This paper gives an overview and a technical comparison of the existing deep learning architectures in handling unstructured Euclidean data for the rapidly developing 3D instance segmentation. First, the authors divide the 3D point clouds based instance segmentation techniques into two major categories which are proposal based methods and proposal free methods. Then, they also introduce and compare the most used datasets with regard to 3D instance segmentation. Furthermore, they compare and analyze these techniques performance (speed, accuracy, response to noise…). Finally, this paper provides a review of the possible future directions of deep learning for 3D sensor-based information and provides insight into the most promising areas for prospective research.

scholarly journals FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3568 ◽  
Author(s):  
Takayuki Shinohara ◽  
Haoyi Xiu ◽  
Masashi Matsuoka
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Lidar Data ◽  
Global Features ◽  
Waveform Data ◽  
Full Waveform ◽  
3D Point Clouds ◽  
Waveform Lidar ◽  
Full Waveform Lidar

In the computer vision field, many 3D deep learning models that directly manage 3D point clouds (proposed after PointNet) have been published. Moreover, deep learning-based-techniques have demonstrated state-of-the-art performance for supervised learning tasks on 3D point cloud data, such as classification and segmentation tasks for open datasets in competitions. Furthermore, many researchers have attempted to apply these deep learning-based techniques to 3D point clouds observed by aerial laser scanners (ALSs). However, most of these studies were developed for 3D point clouds without radiometric information. In this paper, we investigate the possibility of using a deep learning method to solve the semantic segmentation task of airborne full-waveform light detection and ranging (lidar) data that consists of geometric information and radiometric waveform data. Thus, we propose a data-driven semantic segmentation model called the full-waveform network (FWNet), which handles the waveform of full-waveform lidar data without any conversion process, such as projection onto a 2D grid or calculating handcrafted features. Our FWNet is based on a PointNet-based architecture, which can extract the local and global features of each input waveform data, along with its corresponding geographical coordinates. Subsequently, the classifier consists of 1D convolutional operational layers, which predict the class vector corresponding to the input waveform from the extracted local and global features. Our trained FWNet achieved higher scores in its recall, precision, and F1 score for unseen test data—higher scores than those of previously proposed methods in full-waveform lidar data analysis domain. Specifically, our FWNet achieved a mean recall of 0.73, a mean precision of 0.81, and a mean F1 score of 0.76. We further performed an ablation study, that is assessing the effectiveness of our proposed method, of the above-mentioned metric. Moreover, we investigated the effectiveness of our PointNet based local and global feature extraction method using the visualization of the feature vector. In this way, we have shown that our network for local and global feature extraction allows training with semantic segmentation without requiring expert knowledge on full-waveform lidar data or translation into 2D images or voxels.

scholarly journals Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation

2020 ◽  
Vol 9 (9) ◽  
pp. 535
Author(s):  
Francesca Matrone ◽  
Eleonora Grilli ◽  
Massimo Martini ◽  
Marina Paolanti ◽  
Roberto Pierdicca ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cultural Heritage ◽  
Laser Scanning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Classification Algorithms ◽  
Learning Methods ◽  
3D Point Clouds ◽  
The Subject

In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented.

scholarly journals Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage

2020 ◽  
Vol 12 (6) ◽  
pp. 1005 ◽  
Author(s):  
Roberto Pierdicca ◽  
Marina Paolanti ◽  
Francesca Matrone ◽  
Massimo Martini ◽  
Christian Morbidoni ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cultural Heritage ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Information Modeling ◽  
Dynamic Graph ◽  
Historical Building ◽  
Architectural Elements ◽  
3D Point Clouds

In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point Clouds with Deep Learning (DL) techniques can help to recognize historical architectural elements, at an adequate level of detail, and thus speed up the process of modeling of historical buildings for developing BIM models from survey data, referred to as HBIM (Historical Building Information Modeling). In this paper, we propose a DL framework for Point Cloud segmentation, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour. The approach has been applied to a newly collected DCH Dataset which is publicy available: ArCH (Architectural Cultural Heritage) Dataset. This dataset comprises 11 labeled points clouds, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. The involved scenes are both indoor and outdoor, with churches, chapels, cloisters, porticoes and loggias covered by a variety of vaults and beared by many different types of columns. They belong to different historical periods and different styles, in order to make the dataset the least possible uniform and homogeneous (in the repetition of the architectural elements) and the results as general as possible. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.

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 Spatial Aggregation Net: Point Cloud Semantic Segmentation Based on Multi-Directional Convolution

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4329 ◽  
Author(s):  
Guorong Cai ◽  
Zuning Jiang ◽  
Zongyue Wang ◽  
Shangfeng Huang ◽  
Kai Chen ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Point Cloud ◽  
Smart Cities ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Spatial Aggregation ◽  
Structure Information ◽  
Aggregate Information ◽  
3D Point Clouds

Semantic segmentation of 3D point clouds plays a vital role in autonomous driving, 3D maps, and smart cities, etc. Recent work such as PointSIFT shows that spatial structure information can improve the performance of semantic segmentation. Motivated by this phenomenon, we propose Spatial Aggregation Net (SAN) for point cloud semantic segmentation. SAN is based on multi-directional convolution scheme that utilizes the spatial structure information of point cloud. Firstly, Octant-Search is employed to capture the neighboring points around each sampled point. Secondly, we use multi-directional convolution to extract information from different directions of sampled points. Finally, max-pooling is used to aggregate information from different directions. The experimental results conducted on ScanNet database show that the proposed SAN has comparable results with state-of-the-art algorithms such as PointNet, PointNet++, and PointSIFT, etc. In particular, our method has better performance on flat, small objects, and the edge areas that connect objects. Moreover, our model has good trade-off in segmentation accuracy and time complexity.

scholarly journals Review: Deep Learning on 3D Point Clouds

2020 ◽  
Vol 12 (11) ◽  
pp. 1729 ◽  
Author(s):  
Saifullahi Aminu Bello ◽  
Shangshu Yu ◽  
Cheng Wang ◽  
Jibril Muhmmad Adam ◽  
Jonathan Li
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
General Structure ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Point Cloud Data ◽  
3D Vision ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Learning Techniques

A point cloud is a set of points defined in a 3D metric space. Point clouds have become one of the most significant data formats for 3D representation and are gaining increased popularity as a result of the increased availability of acquisition devices, as well as seeing increased application in areas such as robotics, autonomous driving, and augmented and virtual reality. Deep learning is now the most powerful tool for data processing in computer vision and is becoming the most preferred technique for tasks such as classification, segmentation, and detection. While deep learning techniques are mainly applied to data with a structured grid, the point cloud, on the other hand, is unstructured. The unstructuredness of point clouds makes the use of deep learning for its direct processing very challenging. This paper contains a review of the recent state-of-the-art deep learning techniques, mainly focusing on raw point cloud data. The initial work on deep learning directly with raw point cloud data did not model local regions; therefore, subsequent approaches model local regions through sampling and grouping. More recently, several approaches have been proposed that not only model the local regions but also explore the correlation between points in the local regions. From the survey, we conclude that approaches that model local regions and take into account the correlation between points in the local regions perform better. Contrary to existing reviews, this paper provides a general structure for learning with raw point clouds, and various methods were compared based on the general structure. This work also introduces the popular 3D point cloud benchmark datasets and discusses the application of deep learning in popular 3D vision tasks, including classification, segmentation, and detection.

scholarly journals DEEP LEARNING FOR SEMANTIC SEGMENTATION OF 3D POINT CLOUD

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 735-742 ◽  
Author(s):  
E. S. Malinverni ◽  
R. Pierdicca ◽  
M. Paolanti ◽  
M. Martini ◽  
C. Morbidoni ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cultural Heritage ◽  
Point Cloud ◽  
Cultural Landscapes ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Training Data ◽  
Historical Building ◽  
3D Point Clouds

<p><strong>Abstract.</strong> Cultural Heritage is a testimony of past human activity, and, as such, its objects exhibit great variety in their nature, size and complexity; from small artefacts and museum items to cultural landscapes, from historical building and ancient monuments to city centers and archaeological sites. Cultural Heritage around the globe suffers from wars, natural disasters and human negligence. The importance of digital documentation is well recognized and there is an increasing pressure to document our heritage both nationally and internationally. For this reason, the three-dimensional scanning and modeling of sites and artifacts of cultural heritage have remarkably increased in recent years. The semantic segmentation of point clouds is an essential step of the entire pipeline; in fact, it allows to decompose complex architectures in single elements, which are then enriched with meaningful information within Building Information Modelling software. Notwithstanding, this step is very time consuming and completely entrusted on the manual work of domain experts, far from being automatized. This work describes a method to label and cluster automatically a point cloud based on a supervised Deep Learning approach, using a state-of-the-art Neural Network called PointNet++. Despite other methods are known, we have choose PointNet++ as it reached significant results for classifying and segmenting 3D point clouds. PointNet++ has been tested and improved, by training the network with annotated point clouds coming from a real survey and to evaluate how performance changes according to the input training data. It can result of great interest for the research community dealing with the point cloud semantic segmentation, since it makes public a labelled dataset of CH elements for further tests.</p>

scholarly journals INDOOR 3D POINT CLOUDS SEMANTIC SEGMENTATION BASES ON MODIFIED POINTNET NETWORK

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 369-373
Author(s):  
J. Zhao ◽  
X. Zhang ◽  
Y. Wang
Keyword(s):  
Point Cloud ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Local Features ◽  
Indoor Navigation ◽  
Convolution Kernel ◽  
Structural Elements ◽  
Global Features ◽  
3D Point Clouds ◽  
Point Cloud Segmentation

Abstract. Indoor 3D point clouds semantics segmentation is one of the key technologies of constructing 3D indoor models,which play an important role on domains like indoor navigation and positioning,intelligent city, intelligent robot etc. The deep-learning-based methods for point cloud segmentation take on higher degree of automation and intelligence. PointNet,the first deep neural network which manipulate point cloud directly, mainly extracts the global features but lacks of learning and extracting local features,which causes the poor ability of segmenting the local details of architecture and affects the precision of structural elements segmentation . Focusing on the problems above,this paper put forward an automatic end-to-end segmentation method base on the modified PointNet. According to the characteristic that the intensity of different indoor structural elements differ a lot, we input the point cloud information of 3D coordinate, color and intensity into the feature space of points. Also,a MaxPooling is added into the original PointNet network to improve the ability of attracting and learning local features. In addition, replace the 1×1 convolution kernel of original PointNet with 3×3 convolution kernel in the process of attracting features to improve the segmentation precision of indoor point cloud. The result shows that this method improves the automation and precision of indoor point cloud segmentation for the precision achieves over 80% to segment the structural elements like wall,door and so on ,and the average segmentation precision of every structural elements achieves 66%.

scholarly journals 3D Point Cloud Semantic Augmentation: Instance Segmentation of 360° Panoramas by Deep Learning Techniques

2021 ◽  
Vol 13 (18) ◽  
pp. 3647
Author(s):  
Ghizlane Karara ◽  
Rafika Hajji ◽  
Florent Poux
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Point Cloud ◽  
Point Clouds ◽  
Research Field ◽  
Virtual Camera ◽  
3D Point Clouds ◽  
Active Research ◽  
2D Images ◽  
Instance Segmentation

Semantic augmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionised image segmentation and classification, its impact on point cloud is an active research field. In this paper, we propose an instance segmentation and augmentation of 3D point clouds using deep learning architectures. We show the potential of an indirect approach using 2D images and a Mask R-CNN (Region-Based Convolution Neural Network). Our method consists of four core steps. We first project the point cloud onto panoramic 2D images using three types of projections: spherical, cylindrical, and cubic. Next, we homogenise the resulting images to correct the artefacts and the empty pixels to be comparable to images available in common training libraries. These images are then used as input to the Mask R-CNN neural network, designed for 2D instance segmentation. Finally, the obtained predictions are reprojected to the point cloud to obtain the segmentation results. We link the results to a context-aware neural network to augment the semantics. Several tests were performed on different datasets to test the adequacy of the method and its potential for generalisation. The developed algorithm uses only the attributes X, Y, Z, and a projection centre (virtual camera) position as inputs.

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