# DADA: Depth-aware Domain Adaptation in Semantic Segmentation

## TL;DR

This paper introduces a depth-aware unsupervised domain adaptation framework for semantic segmentation that leverages dense depth information from the source domain, significantly improving performance on real-world target data.

## Contribution

The work presents a novel unified framework that exploits dense depth information during training to enhance domain adaptation in semantic segmentation.

## Key findings

- Achieves state-of-the-art results on synthetic-to-real benchmarks.
- Effectively leverages depth information to improve segmentation accuracy.
- Demonstrates significant performance boost over existing methods.

## Abstract

Unsupervised domain adaptation (UDA) is important for applications where large scale annotation of representative data is challenging. For semantic segmentation in particular, it helps deploy on real "target domain" data models that are trained on annotated images from a different "source domain", notably a virtual environment. To this end, most previous works consider semantic segmentation as the only mode of supervision for source domain data, while ignoring other, possibly available, information like depth. In this work, we aim at exploiting at best such a privileged information while training the UDA model. We propose a unified depth-aware UDA framework that leverages in several complementary ways the knowledge of dense depth in the source domain. As a result, the performance of the trained semantic segmentation model on the target domain is boosted. Our novel approach indeed achieves state-of-the-art performance on different challenging synthetic-2-real benchmarks.