# FishNet: A Camera Localizer using Deep Recurrent Networks

## TL;DR

FishNet introduces a deep recurrent network architecture that leverages fisheye camera data and temporal information to improve 6-DOF camera localization accuracy in outdoor environments.

## Contribution

The paper presents a novel RNN-based network architecture with pose regularization for enhanced scene representation and smoother pose estimation in camera localization.

## Key findings

- Effective in outdoor scenery with large overlaps
- Achieves smoother pose estimates
- Outperforms existing methods on benchmark datasets

## Abstract

This paper proposes a robust localization system that employs deep learning for better scene representation, and enhances the accuracy of 6-DOF camera pose estimation. Inspired by the fact that global scene structure can be revealed by wide field-of-view, we leverage the large overlap of a fisheye camera between adjacent frames, and the powerful high-level feature representations of deep learning. Our main contribution is the novel network architecture that extracts both temporal and spatial information using a Recurrent Neural Network. Specifically, we propose a novel pose regularization term combined with LSTM. This leads to smoother pose estimation, especially for large outdoor scenery. Promising experimental results on three benchmark datasets manifest the effectiveness of the proposed approach.