Filtering a Point Cloud to Match the Field of View of the Camera

In a previous post, I described why and how I was collecting a Point Clouds dataset. My setup is depicted in the image above, where a 360°, 32-beam LiDAR is placed above a stereo camera. One of the steps mentioned in the article was to crop (or filter) the Point Cloud to only show points approximatelyContinue reading “Filtering a Point Cloud to Match the Field of View of the Camera”

Semantic Segmentation with Open3D-ML, PyTorch Backend, and a Custom Dataset

Note: Instructions to download, run, and troubleshoot the code introduced in this article are provided at the end. As part of my experimentation with Open3D-ML for Point Clouds, I wrote articles explaining how to install this library with Tensorflow and PyTorch support. To test the installation, I explained how to run a simple Python script to visualize a labeledContinue reading “Semantic Segmentation with Open3D-ML, PyTorch Backend, and a Custom Dataset”

Installing Open3D-ML for 3D Computer Vision with PyTorch

In a previous post, I introduced my reasons to test Open3D-ML and the steps to install it with TensorFlow as the backend. In this post, I go over the steps to install the same library with PyTorch as the backend. Many of the steps are similar but there are some important differences. I hope thisContinue reading “Installing Open3D-ML for 3D Computer Vision with PyTorch”

Testing Open3D-ML for 3D Object Detection and Segmentation

When starting out new research, my approach is usually to test different related things until enough experience allows me to begin connecting the dots. Before I could start building custom models for 3D object detection, I acquired a LiDAR and played around with some data. One next obvious step was to find out how the research worldContinue reading “Testing Open3D-ML for 3D Object Detection and Segmentation”