Abstract: This paper addresses the problem of no reference visual quality assessment in point clouds, useful for extended reality communication service such as remote surgery and education. Accurate, ...

Abstract: 3D point clouds are widely used for robot perception and navigation. LiDAR sensors can provide large scale 3D point clouds (LS3DPC) with a certain level of accuracy in common environment.

Abstract: With the focus on three-dimensional (3D) applications, the importance of applying deep learning to point clouds have been growing recently. It is known that mapping operations including ...

Abstract: With recent success of deep learning in 2-D visual recognition, deep-learning-based 3-D point cloud analysis has received increasing attention from the community, especially due to the rapid ...

Abstract: Self-supervised point cloud representation learning aims to acquire robust and general feature representations from unlabeled data. Recently, masked point modeling-based methods have shown ...

Abstract: The exponential growth of data in the digital age has necessitated the development of frameworks capable of efficiently handling and processing vast datasets. This paper explores the ...

Tutorial facilitation is both an art and a science. During these sessions, learning moves beyond the lecture hall to foster deeper understanding, critical thinking and connections between students.

Abstract: A methodology for creating self-learning tutorials, including tutorials that use screencast technology, is described in this recommended practice. Methods and practices that are applicable ...

Abstract: Learning 3-D structures from incomplete point clouds with extreme sparsity and random distributions is a challenge since it is difficult to infer topological connectivity and structural ...

For much of 2025, the frontier of open-weight language models has been defined not in Silicon Valley or New York City, but in Beijing and Hangzhou. Now, one small U.S. company is pushing back. Today, ...