Review: Deep Learning on 3D Point Clouds

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

Enhanced GenMax Algorithm for Data Mining

Abstract: Association rules mining is an important branch of data mining. Most of association rules mining algorithms make use of only one minimum support to mine items, which may be of different nature. Due to the difference in nature of items, some items may appear less frequent and yet they are very important, and setting a high minimum support may neglect those items. And setting a lower minimum support may result in combinatorial explosion. This result in what is termed as "rare item problem". To address that, many algorithm where developed based on multiple minimum item support, where each item will have its minimum support. In this research paper, a faster algorithm is designed and analyzed and compared to the widely known enhanced Apriori algorithm. An experiment has been conducted and the results showed that the new algorithm can mine out not only the association rules to meet the demands of multiple minimum supports and but also mine out the rare but potentially profitable items' association rules, and is also proved to be faster than the conventional enhanced Apriori

Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing: A survey

Semantic segmentation in 3D meshes is the classification of its constituent element(s) into specific classes or categories. Using the powerful feature extraction abilities of deep neural networks (DNNs), significant results have been obtained in the semantic segmentation of various remotely sensed data formats. With the increased utilization of DNNs to segment remotely sensed data, there have been commensurate in-depth reviews and surveys summarizing the various learning-based techniques and methodologies that entail these methods. However, most of these surveys focused on methods that involve popular data formats like LiDAR point clouds, synthetic aperture radar (SAR) images, and hyperspectral images (HSI) while 3D meshes hardly received any attention. In this paper, to our best knowledge, we present the first comprehensive and contemporary survey of recent advances in utilizing deep learning techniques for the semantic segmentation of urban-scale 3D meshes. We first describe the different approaches employed by mesh-based learning methods to generalize and implement learning techniques on the mesh surface, and then describe how the element-wise classification tasks are achieved through these methods. We also provide an in-depth discussion and comparative analysis of the surveyed methods followed by a summary of the benchmark large-scale mesh datasets accompanied with the evaluation metrics for assessing the segmentation performance of the methods. Finally, we summarize some of the contemporary problems of the field and provide future research directions that may help researchers in the community