A Novel Programmatic Hybrid 2D / 3D Image Segmentation and Labeling Methodology

Abstract

Three-dimensional image segmentation is used in an increasing number of applications, from big-ticket Hollywood films to automated manufacturing to autonomous vehicles. Automated image segmentation relies upon a properly trained deep learning model, which in turn depends upon accurately labelled input. However, the labelling process is very time-intensive and does not lend itself to automation. We set out to automate the labelling process for our specific image content, using Microsoft Kinect RGBD video as input. We created a 3D image segmentation and labelling process that starts with 2D object detection and segmentation. This process is suitable for batch pipeline processing and real-time streaming video. Software demonstrating streaming video processing is shown.