Understanding the Limitations of CNN-based Absolute Camera Pose Regression
Paper i proceeding, 2019

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 selfdriving 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

visual localization

camera pose estimation

deep learning

machine learning


Torsten Sattler

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Qunjie Zhou

Technische Universität München

Marc Pollefeys


Eidgenössische Technische Hochschule Zürich (ETH)

Laura Leal-Taixé

Technische Universität München

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

10636919 (ISSN)

3297-3307 8954331

IEEE / CVF Conference on Computer Vision and Pattern Recognition
Long Beach, USA,


Robotteknik och automation

Datorseende och robotik (autonoma system)



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