CTRL-C: Camera calibration TRansformer with Line-Classification
- URL: http://arxiv.org/abs/2109.02259v1
- Date: Mon, 6 Sep 2021 06:30:38 GMT
- Title: CTRL-C: Camera calibration TRansformer with Line-Classification
- Authors: Jinwoo Lee and Hyunsung Go and Hyunjoon Lee and Sunghyun Cho and
Minhyuk Sung and Junho Kim
- Abstract summary: We propose Camera calibration TRansformer with Line-Classification (CTRL-C), an end-to-end neural network-based approach to single image camera calibration.
Our experiments demonstrate that benchmark-C outperforms the previous state-of-the-art methods on the Google Street View and SUN360 datasets.
- Score: 22.092637979495358
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Single image camera calibration is the task of estimating the camera
parameters from a single input image, such as the vanishing points, focal
length, and horizon line. In this work, we propose Camera calibration
TRansformer with Line-Classification (CTRL-C), an end-to-end neural
network-based approach to single image camera calibration, which directly
estimates the camera parameters from an image and a set of line segments. Our
network adopts the transformer architecture to capture the global structure of
an image with multi-modal inputs in an end-to-end manner. We also propose an
auxiliary task of line classification to train the network to extract the
global geometric information from lines effectively. Our experiments
demonstrate that CTRL-C outperforms the previous state-of-the-art methods on
the Google Street View and SUN360 benchmark datasets.
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