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.

Phishing URL detection has become a critical challenge in cybersecurity, with existing methods often struggling to maintain high accuracy while generalizing across diverse datasets. In this paper, we introduce BERT-PhishFinder, a novel and efficient transformer-based model designed to tackle this problem. While most traditional approaches rely heavily on lexical features or complex convolutional architectures, BERT-PhishFinder leverages the power of DistilBERT, a lightweight yet highly effective transformer, to capture rich contextual representations of URL sequences. To enhance the model’s robustness and reduce overfitting, we strategically incorporate SpatialDropout1D in the embedding layers, along with global average pooling and global max pooling techniques to extract both comprehensive and key discriminative features. The pooled representations are thoughtfully concatenated to form a comprehensive feature representation. Through this carefully crafted design, our model adopts ensemble learning, as it undergoes multiple parallel dense layers, each with distinct parameters and dropout regularization. This facilitates learning diverse patterns and features from the input URL sequence, culminating in exceptional phishing URL detection performance. Extensive evaluations against conventional deep learning algorithms, transformer models (XLNet, RoBERTa, ALBERT), and other existing methods on five benchmark datasets show that BERT-PhishFinder not only achieves the state of-the-art real phishing URL detection but also accomplishes this with reduced label dependency.

Current deep models excel in object detection for classification and localization. However, precise object rotation estimation within the visual context of an input image remains underexplored due to the lack of object datasets with rotation annotations. This paper addresses these challenges by tackling rotation estimation for parked bikes with respect to their parking area. Firstly, 3D graphics were leveraged to build a camera-agnostic well-annotated Synthetic Bike Rotation Dataset (SynthBRSet). Subsequently, an object-to-spot rotation estimator (OSRE) is introduced by extending object detection to regress bike rotations in two axes. As the proposed model trained purely on synthetic data, image smoothing techniques adopted during deployment on real-world images. The proposed OSRE has undergone evaluation on both synthetic and real-world data, showing promising results. Our data and code are available at https://saghiralfasly.github.io/OSRE-Project/.