Understanding the Limitations of CNN-based Absolute Camera Pose Regression

TL;DR

This paper develops a theoretical model to analyze CNN-based camera pose regression, revealing its limitations and showing it often underperforms compared to image retrieval methods, indicating the need for further research.

Contribution

The paper introduces a theoretical framework for understanding CNN-based pose regression and demonstrates its limitations through experiments, highlighting the gap with structure-based methods.

Findings

Pose regression often fails in complex scenes.

Current CNN approaches do not outperform simple image retrieval baselines.

Pose regression is more akin to pose approximation than precise estimation.

Abstract

Visual localization is the task of accurate camera pose estimation in a known scene. It is a key problem in computer vision and robotics, with applications including self-driving cars, Structure-from-Motion, SLAM, and Mixed Reality. Traditionally, the localization problem has been tackled using 3D geometry. Recently, end-to-end approaches based on convolutional neural networks have become popular. These methods learn to directly regress the camera pose from an input image. However, they do not achieve the same level of pose accuracy as 3D structure-based methods. To understand this behavior, we develop a theoretical model for camera pose regression. We use our model to predict failure cases for pose regression techniques and verify our predictions through experiments. We furthermore use our model to show that pose regression is more closely related to pose approximation via image retrieval than to accurate pose estimation via 3D structure. A key result is that current approaches do not consistently outperform a handcrafted image retrieval baseline. This clearly shows that additional research is needed before pose regression algorithms are ready to compete with structure-based methods.