Real-Time 6DOF Pose Relocalization for Event Cameras With Stacked Spatial LSTM Networks

Anh Nguyen, Thanh-Toan Do, Darwin G. Caldwell, Nikos G. Tsagarakis; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


We present a new method to relocalize the 6DOF pose of an event camera solely based on the event stream. Our method first creates the event image from a list of events that occurs in a very short time interval, then a Stacked Spatial LSTM Network (SP-LSTM) is used to learn the camera pose. Our SP-LSTM is composed of a CNN to learn deep features from the event images and a stack of LSTM to learn spatial dependencies in the image feature space. We show that the spatial dependency plays an important role in the relocalization task with event images and the SP-LSTM can effectively learn this information. The extensively experimental results on a publicly available dataset show that our approach outperforms recent state-of-the-art methods by a substantial margin, as well as generalizes well in challenging training/testing splits. The source code and trained models are available at https://github.com/nqanh/pose_relocalization.

Related Material


[pdf]
[bibtex]
@InProceedings{Nguyen_2019_CVPR_Workshops,
author = {Nguyen, Anh and Do, Thanh-Toan and Caldwell, Darwin G. and Tsagarakis, Nikos G.},
title = {Real-Time 6DOF Pose Relocalization for Event Cameras With Stacked Spatial LSTM Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}