Image super-resolution (SR) is one of the vital image processing methods that improve the
resolution of an image in the field of computer vision. In the last two decades, significant …

Super-resolution, the process of obtaining one or more high-resolution images from one or
more low-resolution observations, has been a very attractive research topic over the last two …

Image-guided depth completion aims to generate dense depth maps with sparse depth
measurements and corresponding RGB images. Currently, spatial propagation networks …

In this paper, we consider convolutional neural networks operating on sparse inputs with an
application to depth completion from sparse laser scan data. First, we show that traditional …

Despite the breakthroughs in accuracy and speed of single image super-resolution using
faster and deeper convolutional neural networks, one central problem remains largely …

This paper presents StereoNet, the first end-to-end deep architecture for real-time stereo
matching that runs at 60 fps on an NVidia Titan X, producing high-quality, edge-preserved …

Dense depth perception is critical for autonomous driving and other robotics applications.
However, modern LiDAR sensors only provide sparse depth measurement. It is thus …

This paper introduces an algorithm that accurately estimates depth maps using a lenslet
light field camera. The proposed algorithm estimates the multi-view stereo correspondences …

Depth boundaries often lose sharpness when upsampling from low-resolution (LR) depth
maps especially at large upscaling factors. We present a new method to address the …

We present the bilateral solver, a novel algorithm for edge-aware smoothing that combines
the flexibility and speed of simple filtering approaches with the accuracy of domain-specific …