A Joint 2D-3D Complementary Network for Stereo Matching

Jia, Xiaogang; Chen, Wei; Liang, Zhengfa; Luo, Xin; Wu, Mingxi; Li, Chen; He, Yulin; Tan, Yusong; Huang, Libo

doi:10.3390/s21041430

Cited by 6 publications

(2 citation statements)

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“…In this field, the perception of the environment of unmanned vehicles is particularly important [101,102]. The perception of the model directly affects the quality of decision making and plays a vital role in the safety of unmanned vehicles [103,104]. However, there are many complexities environments in autonomous driving scenes.…”

Section: Deep Learning-based Autonomous Drivingmentioning

confidence: 99%

Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges

Yao

2022

Applied Sciences

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Active learning is a label-efficient machine learning method that actively selects the most valuable unlabeled samples to annotate. Active learning focuses on achieving the best possible performance while using as few, high-quality sample annotations as possible. Recently, active learning achieved promotion combined with deep learning-based methods, which are named deep active learning methods in this paper. Deep active learning plays a crucial role in computer vision tasks, especially in label-insensitive scenarios, such as hard-to-label tasks (medical images analysis) and time-consuming tasks (autonomous driving). However, deep active learning still has some challenges, such as unstable performance and dirty data, which are future research trends. Compared with other reviews on deep active learning, our work introduced the deep active learning from computer vision-related methodologies and corresponding applications. The expected audience of this vision-friendly survey are researchers who are working in computer vision but willing to utilize deep active learning methods to solve vision problems. Specifically, this review systematically focuses on the details of methods, applications, and challenges in vision tasks, and we also introduce the classic theories, strategies, and scenarios of active learning in brief.

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Section: Deep Learning-based Autonomous Drivingmentioning

confidence: 99%

Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges

Yao

2022

Applied Sciences

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“…JDCNet [38] Use the 2D stereo encoder-decoder to generate a disparity range 2D Siamese CNNs & for guiding 3D aggregation network.…”

Section: Cost Volume Regularizationmentioning

confidence: 99%

Optimizing 3D Convolution Kernels on Stereo Matching for Resource Efficient Computations

Xiao

Yamane

2021

Sensors

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Despite recent stereo matching algorithms achieving significant results on public benchmarks, the problem of requiring heavy computation remains unsolved. Most works focus on designing an architecture to reduce the computational complexity, while we take aim at optimizing 3D convolution kernels on the Pyramid Stereo Matching Network (PSMNet) for solving the problem. In this paper, we design a series of comparative experiments exploring the performance of well-known convolution kernels on PSMNet. Our model saves the computational complexity from 256.66G MAdd (Multiply-Add operations) to 69.03G MAdd (198.47G MAdd to 10.84G MAdd for only considering 3D convolutional neural networks) without losing accuracy. On Scene Flow and KITTI 2015 datasets, our model achieves results comparable to the state-of-the-art with a low computational cost.

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