Segment Anything 3D for Point Clouds: Complete Guide

Contents

3D Python How to build a semantic segmentation application for 3D point clouds leveraging SAM and Python. Bonus: code for projections and relationships between 3D points and 2D pixels.

3D Python

How to build a semantic segmentation application for 3D point clouds leveraging SAM and Python. Bonus: code for projections and relationships between 3D points and 2D pixels.

The Segment Anything Model for 3D Environments. We will detect objects in indoor spaces using 3D point cloud datasets by Florent Poux and mimatelier — The Segment Anything Model for 3D Environments. We will detect objects in indoor spaces using 3D point cloud datasets. Credit goes to Mimatelier, the talented illustrator who created this image.

Technological leaps are just plain crazy, especially looking at Artificial Intelligence (AI) applied to 3D challenges. Having the ability to leverage the latest cutting-edge research for advanced 3D applications is very empowering. Especially when looking at bringing human-level reasoning capabilities to a computer, there is a clear need to extract a formalized meaning from the 3D entities that we observe.

In this tutorial, we are here to make sure that we can bind amazing AI advancements with 3D applications that make use of 3D Point Clouds. — 🐲 Florent & Ville

This is no easy feat, but once mastered, the fusion of 3D point clouds and deep learning gives birth to new dimensions of understanding and interpreting our visual world.

Among these advancements, the Segment Anything Model is a recent beacon of innovation, especially for full automation without supervision.