References of "Nurunnabi, Abdul Awal Md 50039774"
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See detailINVESTIGATION OF POINTNET FOR SEMANTIC SEGMENTATION OF LARGE-SCALE OUTDOOR POINT CLOUDS
Nurunnabi, Abdul Awal Md UL; Teferle, Felix Norman UL; Li, J. et al

Scientific journal (2021)

Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have credibility for capturing geometry of objects including shape, size, and orientation. Deep learning (DL ... [more ▼]

Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have credibility for capturing geometry of objects including shape, size, and orientation. Deep learning (DL) has been recognized as the most successful approach for image semantic segmentation. Applied to point clouds, performance of the many DL algorithms degrades, because point clouds are often sparse and have irregular data format. As a result, point clouds are regularly first transformed into voxel grids or image collections. PointNet was the first promising algorithm that feeds point clouds directly into the DL architecture. Although PointNet achieved remarkable performance on indoor point clouds, its performance has not been extensively studied in large-scale outdoor point clouds. So far, we know, no study on large-scale aerial point clouds investigates the sensitivity of the hyper-parameters used in the PointNet. This paper evaluates PointNet’s performance for semantic segmentation through three large-scale Airborne Laser Scanning (ALS) point clouds of urban environments. Reported results show that PointNet has potential in large-scale outdoor scene semantic segmentation. A remarkable limitation of PointNet is that it does not consider local structure induced by the metric space made by its local neighbors. Experiments exhibit PointNet is expressively sensitive to the hyper-parameters like batch-size, block partition and the number of points in a block. For an ALS dataset, we get significant difference between overall accuracies of 67.5% and 72.8%, for the block sizes of 5m×5m and 10m×10m, respectively. Results also discover that the performance of PointNet depends on the selection of input vectors. [less ▲]

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See detailAn efficient deep learning approach for ground point filtering in aerial laser scanning point clouds
Nurunnabi, Abdul Awal Md UL

in Nurunnabi, Abdul Awal Md; Teferle, Felix Norman; Li, Jonathan (Eds.) et al An efficient deep learning approach for ground point filtering in aerial laser scanning point clouds (2021, July 02)

Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and ... [more ▼]

Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management. Many methods have been developed over the last three decades. Recently, Deep Learning (DL) has become the most dominant technique for 3D point cloud classification. DL methods used for classification can be categorized into end-to-end and non end-to-end approaches. One of the main challenges of using supervised DL approaches is getting a sufficient amount of training data. The main advantage of using a supervised non end-to-end approach is that it requires less training data. This paper introduces a novel local feature-based non end-to-end DL algorithm that generates a binary classifier for ground point filtering. It studies feature relevance, and investigates three models that are different combinations of features. This method is free from the limitations of point clouds’ irregular data structure and varying data density, which is the biggest challenge for using the elegant convolutional neural network. The new algorithm does not require transforming data into regular 3D voxel grids or any rasterization. The performance of the new method has been demonstrated through two ALS datasets covering urban environments. The method successfully labels ground and non-ground points in the presence of steep slopes and height discontinuity in the terrain. Experiments in this paper show that the algorithm achieves around 97% in both F1-score and model accuracy for ground point labelling. [less ▲]

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See detailA boundary-enhanced supervoxel method for extraction of road edges in MLS point clouds
Sha, Zhengchuan; Chen, Yiping; Li, Wen et al

in A boundary-enhanced supervoxel method for extraction of road edges in MLS point clouds (2020)

Road extraction plays a significant role in production of high definition maps (HD maps). This paper presents a novel boundary-enhanced supervoxel segmentation method for extracting road edge contours ... [more ▼]

Road extraction plays a significant role in production of high definition maps (HD maps). This paper presents a novel boundary-enhanced supervoxel segmentation method for extracting road edge contours from MLS point clouds. The proposed method first leverages normal feature judgment to obtain 3D point clouds global geometric information, then clusters points according to an existing method with global geometric information to enhance the boundaries. Finally, it utilizes the neighbor spatial distance metric to extract the contours and drop out existing outliers. The proposed method is tested on two datasets acquired by a RIEGL VMX-450 MLS system that contain the major point cloud scenes with different types of road boundaries. The experimental results demonstrate that the proposed method provides a promising solution for extracting contours efficiently and completely. Results show that the precision values are 1.5 times higher and approximately equal than the other two existing methods when the recall value is 0 for both tested two road datasets. [less ▲]

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