The barrier to entry has never been lower, and you'll own your password manager in every meaningful way. Even if Bitwarden's ...
Abstract: Quantum computing is a fascinating interdisciplinary research field that promises to revolutionize computing by efficiently solving previously intractable problems. Recent years have seen ...
Abstract: This paper proposes an unsupervised learning framework based on the Point-Move network to address the issue of converting point cloud data to high-quality surface meshes in the field of 3D ...
Abstract: Pose prediction and trajectory forecasting represent pivotal tasks in the realm of autonomous driving, crucially enhancing the planning and decision-making capabilities of self-driving ...
Abstract: Recent learning-based models excel in point cloud registration for low-overlap scenes but falter in scenarios with minimal overlap. In this article, we propose a novel method to address the ...
Abstract: Recently, three-dimensional (3D) point-cloud analysis has been extensively utilized in the domain of machine vision, encompassing tasks include shape classification and segmentation. However ...
Safety-critical systems are all around us in the modern world, from autonomous cars to aerial systems. In recent years, Control Barrier Functions (CBFs) [1] have proven to be a versatile method for ...
Abstract: Split computing (SC) is an emerging technique to perform the inference task of deep neural network (DNN) models using both mobile devices and cloud/edge servers in a hybrid manner. To ...
Morning Overview on MSN
iPhone storage full? Here’s what ‘System Data’ really is
When an iPhone suddenly refuses to install a new app or take another photo, the culprit is often a mysterious bar in Settings ...
Robust Point Cloud Registration in Robotic Inspection With Locally Consistent Gaussian Mixture Model
Abstract: In robotic inspection of aviation parts, achieving accurate pairwise point cloud registration between scanned and model data is essential. However, noise and outliers generated in robotic ...
Abstract: Convolutional neural networks (CNNs) are known for their exceptional performance in various applications; however, their energy consumption during inference can be substantial. Analog ...
Abstract: Deep learning methods have recently shown significant promise in compressing the geometric features of point clouds. However, challenges arise when consecutive point clouds contain holes, ...
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