Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video with Applications for Virtual Reality

TL;DR

This paper presents a CNN-based method for unsupervised depth and ego-motion estimation from cylindrical panoramic videos, enhancing virtual reality applications by leveraging a novel projection and new datasets.

Contribution

The authors introduce a cylindrical panoramic projection for CNNs, enabling effective unsupervised learning of depth and ego-motion from panoramic videos, along with new datasets for evaluation.

Findings

High-quality depth maps from synthetic and real data

Field-of-view increase improves ego-motion accuracy

Effective monocular to stereo panorama conversion

Abstract

We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. Panoramic depth estimation is an important technology for applications such as virtual reality, 3D modeling, and autonomous robotic navigation. In contrast to previous approaches for applying convolutional neural networks to panoramic imagery, we use the cylindrical panoramic projection which allows for the use of the traditional CNN layers such as convolutional filters and max pooling without modification. Our evaluation of synthetic and real data shows that unsupervised learning of depth and ego-motion on cylindrical panoramic images can produce high-quality depth maps and that an increased field-of-view improves ego-motion estimation accuracy. We create two new datasets to evaluate our approach: a synthetic dataset created using the CARLA simulator, and Headcam, a novel dataset of panoramic video collected from a helmet-mounted camera while biking in an urban setting. We also apply our network to the problem of converting monocular panoramas to stereo panoramas.