Abstract: Readily available RGB-D cameras in smart phones and improving 3D scanning technologies have made it possible to produce detailed point cloud and point-based models of real world objects even ...

That being the case, a handy tool to have in one’s repertoire is a way to work with point clouds. We’ll explain why in a moment, but that’s where CloudCompare comes in (GitHub).

Point cloud completion aims to acquire complete and high-fidelity point clouds from partial and low-quality point clouds, which are used in remote sensing applications. Existing methods tend to solve ...

The automotive industry is utilizing LiDAR point cloud processing technologies for real-time object detection, 3D scene reconstruction, high-definition mapping, and semantic segmentation.

This article explains how to use a PyTorch neural autoencoder to find anomalies in a dataset. A good way to see where this article is headed is to take a look at the screenshot of a demo program in ...

SDS-Complete leverages a pre-trained text-to-image diffusion model to guide the completion of missing parts in point clouds. Traditional approaches to point cloud completion rely heavily on ...

Point-E doesn’t create 3D objects in the traditional sense. Rather, it generates point clouds, or discrete sets of data points in space that represent a 3D shape — hence the cheeky abbreviation.