FADNet++: Real-Time and Accurate Disparity Estimation with Configurable
Networks
- URL: http://arxiv.org/abs/2110.02582v1
- Date: Wed, 6 Oct 2021 08:50:33 GMT
- Title: FADNet++: Real-Time and Accurate Disparity Estimation with Configurable
Networks
- Authors: Qiang Wang, Shaohuai Shi, Shizhen Zheng, Kaiyong Zhao, Xiaowen Chu
- Abstract summary: FADNet++ is an efficient deep network for disparity estimation.
It can boost its accuracy with a fast model inference speed for real-time applications.
It achieves a new state-of-the-art result for the SceneFlow dataset.
- Score: 19.29846600092521
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep neural networks (DNNs) have achieved great success in the area of
computer vision. The disparity estimation problem tends to be addressed by DNNs
which achieve much better prediction accuracy than traditional hand-crafted
feature-based methods. However, the existing DNNs hardly serve both efficient
computation and rich expression capability, which makes them difficult for
deployment in real-time and high-quality applications, especially on mobile
devices. To this end, we propose an efficient, accurate, and configurable deep
network for disparity estimation named FADNet++. Leveraging several liberal
network design and training techniques, FADNet++ can boost its accuracy with a
fast model inference speed for real-time applications. Besides, it enables
users to easily configure different sizes of models for balancing accuracy and
inference efficiency. We conduct extensive experiments to demonstrate the
effectiveness of FADNet++ on both synthetic and realistic datasets among six
GPU devices varying from server to mobile platforms. Experimental results show
that FADNet++ and its variants achieve state-of-the-art prediction accuracy,
and run at a significant order of magnitude faster speed than existing 3D
models. With the constraint of running at above 15 frames per second (FPS) on a
mobile GPU, FADNet++ achieves a new state-of-the-art result for the SceneFlow
dataset.
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