Artificial Intelligence and Machine Vision

This blog is about how to train a neural network to extract depth maps from videos of moving people captured with a monocular camera. Note: With a monocular camera, extracting the depth map of moving people is difficult.  Difficulty is due to the motion blur and the rolling shutter of an image.  However, we can overcome these limitations by predicting the depth maps by the model trained with a generated dataset using SfM and MVS from the normalized videos. This normalized dataset can be the basis of the training set for the neural network to automatically extract the accurate depth maps from a typical video footage, without any further assistance from a MVS. To start this project with a SfM and a MVS, we will use TUM Dataset. So, the basic idea is to use SfM and Multiview Stereo to estimate depth, while serves as supervision during training. The RGB-D SLAM reference implementation from these papers are used: - RGB-D Slam (Robotics OS) - Real-time 3D Visual SLAM with a ha