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”

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”

Normalizing (Feature Scaling) Point Clouds for Machine Learning

Continuing my work on Machine Learning with point clouds in the realm of autonomous robots, and coming from working with image data, I was faced with the following question: does 3D data need normalization like image data does? The answer is a clear YES (duh!). Normalization, or feature scaling, is an important preprocessing step for many machineContinue reading “Normalizing (Feature Scaling) Point Clouds for Machine Learning”

Simplifying Point Cloud Labeling with Contextual Images and Point Cloud Filtering

Annotating point clouds from multi-line 360° LiDAR is exceedingly difficult. Providing context in the form of camera frames and limiting the point cloud to the Field Of View (FOV) of the camera simplifies things. To achieve this, we first had to replace our old, and not so stable LiDAR mount, with a sturdier one capableContinue reading “Simplifying Point Cloud Labeling with Contextual Images and Point Cloud Filtering”

Testing 3D Annotation Tools

Before you can train a supervised Deep Learning model, you must first label your data. Today I am testing Intel OpenVINO’s CVAT and MATLAB’s Lidar Labeler annotation tools for 3D data. First impressions, CVAT makes it easier to navigate the point cloud, but a small bug makes it hard to place the initial cuboid, makingContinue reading “Testing 3D Annotation Tools”