Demo Abstract: Online Training and Inference for On-Device Monocular Depth Estimation

For a robot deployed in the world, it is desirable to have the ability of autonomous learning to improve its initial pre-set knowledge. We formalize this as a bootstrapped self-supervised learning problem where a system is initially bootstrapped with ...

We present an unsupervised learning method for the task of monocular depth estimation. In common with many recent works, we leverage convolutional neural network (CNN) training on stereo pair images with view reconstruction as a self-supervisory ...

All depth data captured by Kinect devices are noisy, and sometimes even lost or shifted, especially around the edges of the depth. In this paper, we propose an approach to generate a per-pixel confidence measurement for each depth map captured by Kinect ...