# Relative Geometry-Aware Siamese Neural Network for 6DOF Camera Relocalization

## TL;DR

This paper introduces a novel Siamese neural network that leverages relative geometry constraints and multi-task learning to improve 6DOF camera relocalization accuracy in both indoor and outdoor environments.

## Contribution

It proposes a relative geometry-aware Siamese network with a new adaptive metric loss, enhancing pose estimation by exploiting geometric relationships between images.

## Key findings

- Significantly improves localization accuracy on benchmark datasets.
- Effective in both indoor and outdoor scenarios.
- Ablation studies confirm the importance of each loss component.

## Abstract

6DOF camera relocalization is an important component of autonomous driving and navigation. Deep learning has recently emerged as a promising technique to tackle this problem. In this paper, we present a novel relative geometry-aware Siamese neural network to enhance the performance of deep learning-based methods through explicitly exploiting the relative geometry constraints between images. We perform multi-task learning and predict the absolute and relative poses simultaneously. We regularize the shared-weight twin networks in both the pose and feature domains to ensure that the estimated poses are globally as well as locally correct. We employ metric learning and design a novel adaptive metric distance loss to learn a feature that is capable of distinguishing poses of visually similar images from different locations. We evaluate the proposed method on public indoor and outdoor benchmarks and the experimental results demonstrate that our method can significantly improve localization performance. Furthermore, extensive ablation evaluations are conducted to demonstrate the effectiveness of different terms of the loss function.