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Detecting low-rank regions in omnidirectional images
at
| Zoltan Kato, Gabor Nagy, Martin Humenberger, Gabriela Csurka |
| Omnidirectional Computer Vision Workshop at the Conference on Computer Vision and Pattern Recognition (CVPR), virtual event, 16-25 June, 2021 |
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Planar low-rank regions commonly found in man-made environments, can be used to estimate a rectifying homography that provides valuable information about the camera and the 3D plane they observe. Methods to recover such a homography exist, but detection of low-rank regions is largely unsolved, especially for omnidirectional cameras where significant distortions make the problem even more challenging. In this paper we address this problem as follows. First we propose a method to generate a low-rank probability map on an omnidirectional image and use it to build a training set in a self-supervised manner to train deep models to predict low-rank likelihood maps for omnidirec- tional images. Second, we propose to adapt regular CNN operators to equirectangular images and to combine them seamlessly into a network where each layer preserves the properties of the equirectangular representation. Finally, on the new KITTI360 dataset, we show that the rectifying homography of detected low-rank regions in such predicted maps allows to factorize out the camera-plane pose up to certain ambiguities that can be easily overcome.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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