# Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video

## TL;DR

This paper presents a CNN-based method for unsupervised depth and ego-motion estimation from cylindrical panoramic videos, leveraging the projection to improve accuracy and introducing a new dataset for urban biking scenarios.

## Contribution

It introduces a novel CNN model that uses cylindrical projection for panoramic video, enabling effective unsupervised depth and ego-motion learning without modifications to standard CNN layers.

## Key findings

- High-quality depth maps achieved from synthetic and real data.
- Wider field-of-view improves ego-motion estimation accuracy.
- Introduces Headcam dataset for urban panoramic video analysis.

## 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 also introduce Headcam, a novel dataset of panoramic video collected from a helmet-mounted camera while biking in an urban setting.