8 accepted papers
Depth completion is a popular research direction in the field of depth estimation. The fusion of color and depth features is the critical challenge in this task, mainly due to the asymmetry between the rich scene details in color images and the sparse pixels in depth maps. To tackle this issue, we d…
Cutting-edge real-time detectors usually reach real-time performance by adopting lightweight architectures. The accuracy of detection may be limited by their insufficient capabilities to obtain powerful feature representation, which is a notoriously onerous task in machine vision applications. Aimin…
The dimensions of cost volumes and corresponding aggregation networks play a critical role in balancing the speed and accuracy for stereo matching. Current most 2D stereo networks based on 3D cost volumes are speedy but poor in precision, while 3D stereo networks with 4D volumes are slow but precise
Stereo matching has attracted much attention in recent years. Traditional methods can quickly generate a disparity result, but the accuracy is low. On the contrary, methods based on neural networks can achieve a high accuracy level, but they are difficult to reach the real-time level. Therefore, thi…
Stereo matching is essential for robot navigation. However, the accuracy of current widely used traditional methods is low, while methods based on CNN need expensive computational cost and running time. This is because different cost volumes play a crucial role in balancing speed and accuracy. Thus…
Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint. However, it is challenging to incorporate this information for SR since disparities between stereo images vary significantly. In this paper, we propose…
Exploiting multi-level context information to cost volume can improve the performance of learning-based stereo matching methods. In recent years, 3-D Convolution Neural Networks (3-D CNNs) show the advantages in regularizing cost volume but are limited by unary features learning in matching cost com…
Stereo matching algorithms usually consist of four steps, including matching cost calculation, matching cost aggregation, disparity calculation, and disparity refinement. Existing CNN-based methods only adopt CNN to solve parts of the four steps, or use different networks to deal with different step…