Domain Adaptation of Learned Features for Visual Localization

TL;DR

This paper introduces a few-shot domain adaptation framework for learned local features to improve visual localization accuracy across varying conditions with minimal target domain data.

Contribution

It presents a novel few-shot domain adaptation approach specifically designed for learned local features in visual localization tasks.

Findings

Outperforms baseline methods in diverse conditions

Requires only a few target domain examples

Demonstrates robustness to environmental changes

Abstract

We tackle the problem of visual localization under changing conditions, such as time of day, weather, and seasons. Recent learned local features based on deep neural networks have shown superior performance over classical hand-crafted local features. However, in a real-world scenario, there often exists a large domain gap between training and target images, which can significantly degrade the localization accuracy. While existing methods utilize a large amount of data to tackle the problem, we present a novel and practical approach, where only a few examples are needed to reduce the domain gap. In particular, we propose a few-shot domain adaptation framework for learned local features that deals with varying conditions in visual localization. The experimental results demonstrate the superior performance over baselines, while using a scarce number of training examples from the target domain.