# Guided Depth Upsampling for Precise Mapping of Urban Environments

## TL;DR

This paper introduces a novel MRF-based depth upsampling method guided by image and 3D surface normal features, leveraging camera models to improve urban environment mapping accuracy.

## Contribution

The paper proposes a new regularization term based on surface planarity, enhancing depth upsampling in urban scenes with predominantly planar surfaces.

## Key findings

- Outperforms recent distance-based regularization methods on synthetic data
- Improves depth upsampling quality in urban environments
- Validated on real mapping applications with an experimental vehicle

## Abstract

We present an improved model for MRF-based depth upsampling, guided by image- as well as 3D surface normal features. By exploiting the underlying camera model we define a novel regularization term that implicitly evaluates the planarity of arbitrary oriented surfaces. Our method improves upsampling quality in scenes composed of predominantly planar surfaces, such as urban areas. We use a synthetic dataset to demonstrate that our approach outperforms recent methods that implement distance-based regularization terms. Finally, we validate our approach for mapping applications on our experimental vehicle.