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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Convolutional Cross-View Pose Estimation.

Zimin Xia, Olaf Booij, Julian F P Kooij

IEEE Transactions on Pattern Analysis and Machine Intelligence

|December 25, 2023

Summary

This summary is machine-generated.

This study introduces a new method for cross-view pose estimation, accurately determining camera location and orientation from ground and aerial images. The approach significantly improves accuracy, outperforming existing methods for precise localization.

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Area of Science:

Computer Vision
Robotics
Geospatial Analysis

Background:

Cross-view pose estimation is crucial for applications like autonomous driving and robotics.
Existing methods often struggle with accuracy and handling localization ambiguity.

Purpose of the Study:

To develop an end-to-end method for accurate 3 Degrees-of-Freedom (3DoF) camera pose estimation using ground-level and aerial imagery.
To improve localization accuracy and handle ambiguity in cross-view scenarios.

Main Methods:

Utilizing orientation-aware image descriptors generated via a translationally equivariant convolutional encoder and contrastive learning.
Employing a novel Localization Decoder with a Localization Matching Upsampling module for coarse-to-fine probability distribution.
Integrating an Orientation Decoder to condition orientation estimation on localization.

Main Results:

Achieved superior performance on VIGOR and KITTI datasets, surpassing state-of-the-art baselines by 72% and 36% in median localization error.
Demonstrated reliable ego-vehicle pose estimation on Oxford RobotCar dataset with sub-meter localization and 1-degree orientation accuracy at 14 FPS.
The method effectively represents and rejects localization ambiguity through a predicted probability distribution.

Conclusions:

The proposed method offers a robust and accurate solution for cross-view pose estimation.
The approach demonstrates versatility, adapting to different image fields of view and utilizing orientation priors without retraining.
This technique has significant potential for real-world applications in robotics and autonomous navigation.