Self-similarity based super-resolution (SR) algorithms are able to produce visually pleasing
results without extensive training on external databases. Such algorithms exploit the …

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 …

Depth completion, the technique of estimating a dense depth image from sparse depth
measurements, has a variety of applications in robotics and autonomous driving. However …

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

With the rise of data driven deep neural networks as a realization of universal function
approximators, most research on computer vision problems has moved away from …

The recent phenomenal interest in convolutional neural networks (CNNs) must have made it
inevitable for the super-resolution (SR) community to explore its potential. The response has …

Rapid development of affordable and portable consumer depth cameras facilitates the use
of depth information in many computer vision tasks such as intelligent vehicles and 3D …

Despite the remarkable progresses made in deep learning based depth map super-
resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps …

Recently, consumer depth cameras have gained significant popularity due to their affordable
cost. However, the limited resolution and the quality of the depth map generated by these …

In this work we present a novel approach for single depth map super-resolution. Modern
consumer depth sensors, especially Time-of-Flight sensors, produce dense depth …