Several computer vision tasks require perceiving or interacting with 3D environments and objects therein, making a strong case in favor of 3D deep learning. However, unlike images which are most ...

Few-shot classification for 3D point clouds remains a challenging task, primarily because real-world data is often noisy and incomplete, complicating robust feature learning from limited samples. Real ...

Abstract: Point clouds are a set of data points in space to represent the 3D geometry of objects. A fundamental step in the processing is to identify a subset of points to represent the shape. While ...

This project implements GP-PCS (Gaussian Process Point Cloud Simplification) — a state-of-the-art method to drastically reduce the number of points in 3D point clouds while preserving essential ...

In the coming decades, it seems inevitable that architects will increasingly focus on renovations and rehabilitations –especially in established urban centers–, whether to modernize outdated ...

Point-E, unlike similar systems, "leverages a large corpus of (text, image) pairs, allowing it to follow diverse and complex prompts, while our image-to-3D model is trained on a smaller dataset of ...

3D scanning is becoming much more accessible, which means it’s more likely that the average hacker will use it to solve problems — possibly odd ones. That being the case, a handy tool to have in one’s ...