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Author  |
Charco, J.L., Sappa, A.D., Vintimilla, B.X., Velesaca, H.O. |
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Title |
Camera pose estimation in multi-view environments:from virtual scenarios to the real world |
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Journal Article |
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Year |
2021 |
Publication |
In Image and Vision Computing Journal. (Article number 104182) |
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Vol. 110 |
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Keywords |
Relative camera pose estimation, Domain adaptation, Siamese architecture, Synthetic data, Multi-view environments |
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Abstract |
This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired
images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of
overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly
trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the
relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity
on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the
scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used,
highlighting the importance on the similarity between virtual-real scenarios. |
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English |
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no |
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Call Number |
cidis @ cidis @ |
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147 |
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Author |
Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa |
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Title |
Fast CNN Stereo Depth Estimation through Embedded GPU Devices |
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Journal Article |
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Year |
2020 |
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Sensors 2020 |
Abbreviated Journal |
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Vol. 2020-June |
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11 |
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pp. 1-13 |
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Keywords |
stereo matching; deep learning; embedded GPU |
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Abstract |
Current CNN-based stereo depth estimation models can barely run under real-time
constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art
evaluations usually do not consider model optimization techniques, being that it is unknown what is
the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models
on three different embedded GPU devices, with and without optimization methods, presenting
performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth
estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture
for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically
augmenting the runtime speed of current models. In our experiments, we achieve real-time inference
speed, in the range of 5–32 ms, for 1216 368 input stereo images on the Jetson TX2, Jetson Xavier,
and Jetson Nano embedded devices. |
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14248220 |
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no |
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cidis @ cidis @ |
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132 |
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Author |
Cristhian A. Aguilera; Angel D. Sappa; Ricardo Toledo |
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Title |
Cross-Spectral Local Descriptors via Quadruplet Network |
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Journal Article |
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2017 |
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In Sensors Journal |
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Vol. 17 |
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pp. 873 |
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no |
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gtsi @ user @ |
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64 |
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Author |
Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa |
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Title |
Melamine faced panels defect classification beyond the visible spectrum. |
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2018 |
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In Sensors 2018 |
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Vol. 11 |
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Issue 11 |
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Abstract |
In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond
the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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gtsi @ user @ |
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89 |
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