Publication Details
Estimating Extreme 3D Image Rotations using Cascaded Attention
camera orientation estimation, extreme rotation, 3D rotation, cascaded attention
Estimating large, extreme inter-image rotations is critical for numerous computer
vision domains involving images related by limited or non-overlapping fields of
view. In this work, we propose an attention-based approach with a pipeline of
novel algorithmic components. First, as rotation estimation pertains to image
pairs, we introduce an inter-image distillation scheme using Decoders to improve
embeddings. Second, whereas contemporary methods compute a 4D correlation volume
(4DCV) encoding inter-image relationships, we propose an Encoder-based
cross-attention approach between activation maps to compute an enhanced
equivalent of the 4DCV. Finally, we present a cascaded Decoder-based technique
for alternately refining the cross-attention and the rotation query. Our approach
outperforms current state-of-the-art methods on extreme rotation estimation. We
make our code publicly available.
@inproceedings{BUT188275,
author="Shay {Dekel} and Yosi {Keller} and Martin {Čadík}",
title="Estimating Extreme 3D Image Rotations using Cascaded Attention",
booktitle="Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
year="2024",
pages="2588--2598",
publisher="IEEE Computer Society",
address="Seattle",
doi="10.1109/CVPR52733.2024.00250",
isbn="979-8-3503-5301-3",
url="https://cadik.posvete.cz/"
}