Recovering point clouds involves the sequential process of sampling and restoration, yet existing methods struggle to effectively leverage both topological and geometric attributes. To address this, we propose an end-to-end architecture named TopGeoFormer, which maintains these critical properties throughout the sampling and restoration phases. First, we revisit traditional feature extraction techniques to yield topological embedding using a continuous mapping of relative relationships between neighboring points, and integrate it in both phases for preserving the structure of the original space. Second, we propose the InterTwining Attention to fully merge topological and geometric embeddings, which queries shape with local awareness in both phases to form a learnable 3D shape context facilitated with point-wise, point-shape-wise, and intra-shape features. Third, we introduce a full geometry loss and a topological constraint loss to optimize the embeddings in both Euclidean and topological spaces. The geometry loss uses inconsistent matching between coarse-to-fine generations and targets for reconstructing better geometric details, and the constraint loss limits embedding variances for better approximation of the topological space. In experiments, we comprehensively analyze the circumstances using the conventional and learning-based sampling/upsampling/recovery algorithms. The quantitative and qualitative results demonstrate that our method significantly outperforms existing sampling and recovery methods.

Incomplete point clouds that are generally scanned with flaws or large gaps can cause to inaccurate predictions by learning-based classification methods. To address this issue, in this paper, we propose an end-to-end architecture that compensates for and identifies partial point clouds on the fly. First, we propose a cascaded solution that integrates both the upstream and downstream networks simultaneously, allowing the task-oriented downstream to identify the points generated by the completion-oriented upstream. These two streams complement each other, resulting in improved performance for both completion and downstream-dependent tasks. Second, to explicitly understand the predicted points’ pattern, we introduce hierarchical self-distillation (HSD), which can be applied to arbitrary hierarchy-based point cloud methods. On the classification task, our proposed method performs competitively on the synthetic dataset and achieves superior results on the challenging real-world benchmark when compared to the state-of-the-art models.

In recent years, to enhance all-weather observation capabilities, remote sensing platforms have been increasingly equipped with thermal infrared (TIR) sensors in addition to visible spectrum (RGB) sensors. Consequently, remote sensing object detection methods started utilizing images from both modalities to improve detection accuracy. However, the inconsistency of target visibility across the two spectrums would introduce confusion of the multi-spectral model, leading to lower detection performance compared to the single-spectral TIR model. In this study, a fine-grained cross-teaching method is proposed to mitigate confusion caused by visibility inconsistency. In detail, leveraging the one-to-one matching mechanism of Detection Transformer (DETR), if a target is visible in both images, knowledge is distilled from a multi-spectral DETR to a TIR-only DETR. Conversely, if an object is only visible in the TIR image, reverse distillation is conducted. Experiments show that cross teaching aids both the single-spectral and multi-spectral models, achieving the state-of-the-art performance on the DroneVehicle dataset.

In the real scene of 3D object detection, the point cloud collected for a single object is incomplete, resulting in misalignment of classification and regression features and uncertainty of object boundaries. Existing works pay little attention to the uncertainty caused by the incompleteness of point clouds. To address this issue, we propose a Feature Decoupling and Uncertainty Estimation single-stage 3D object detector named FDUE-Net. First, we design a Classification-Regression Attention Decoupling module to extract shared low-level and high-level features in a decoupling paradigm, generating the feature maps that are more helpful for classification or regression by layer-level and space-level attention modules. Furthermore, we propose an Uncertainty Estimation head (UE-head), which improves the quality of the predicted bounding boxes by modeling the regression values as general distributions, and uses the predicted distributions to correct the classification scores. Experiments on the KITTI dataset show that the proposed method achieves significant improvement, with 3D detection performance improved by 1.46% on the moderate set compared to the baseline, and competitive performance compared to the state-of-the-arts.

Credit card fraud has become increasingly common despite advancements in credit card technology aimed at enhancing security. Various forms of fraud, such as stolen or lost cards, as well as hacking into digital payment methods like Apple Pay and Google Pay, contribute to this issue. As a result, it is crucial to detect and prevent credit card fraud, particularly from the perspective of credit card issuers. Machine Learning offers a promising approach to address this challenge, specifically through the application of classification algorithms. These algorithms can analyze a set of variables or a single variable and assign it to a particular class, making them suitable for identifying fraudulent and non-fraudulent credit card transactions. This research paper presents the outcomes of implementing multiple machine learning algorithms to classify credit card transactions as either fraudulent or non-fraudulent. The dataset used in this study comprises credit card transactions made by European cardholders during a two-day period in September 2013. We start with analysing the data of the transactions, building the classification models for detecting fraud, comparing the results of the models built, comparing the performance of the best model with the models researched previously and finally choosing the best model for the fraud detection. In this research paper, we used various classification algorithms and to improve the accuracy of the models, we had used three class balancing techniques which are Random Oversampling, SMOTE and ADASYN. We have found that Random Forest model with ADASYN class imbalancing technique is the best model for fraud detection as it has and ROC-AUC score of 0.99 on the test dataset.

Recent supervised point cloud upsampling methods are re-stricted by the size of training data and are limited in terms of covering all object shapes. Besides the challenges faced due to data acquisition, the networks also struggle to gener-alize on unseen records. In this paper, we present an internal point cloud upsampling approach at a holistic level referred to as “Zero-Shot” Point Cloud Upsampling (ZSPU). Our approach is data agnostic and relies solely on the internal infor-mation provided by a particular point cloud without patching in both self-training and testing phases. This single-stream design significantly reduces the training time by learning the relation between low resolution (LR) point clouds and their high (original) resolution (HR) counterparts. This association will then provide super resolution (SR) outputs when origi-nal point clouds are loaded as input. ZSPU achieves com-petitive/superior quantitative and qualitative performances on benchmark datasets when compared with other upsampling methods.