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 …

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 …

Depth map super-resolution is an ill-posed inverse problem with many challenges. First,
depth boundaries are generally hard to reconstruct particularly at large magnification factors …

Depth image super-resolution is a significant yet challenging task. In this paper, we
introduce a novel deep color guided coarse-to-fine convolutional neural network (CNN) …

Depth map super-resolution is a task with high practical application requirements in the
industry. Existing color-guided depth map super-resolution methods usually necessitate an …

This paper describes a new algorithm for depth image super resolution and denoising using
a single depth image as input. A robust coupled dictionary learning method with locality …

Depth map upsampling will unavoidably smoothen the edges leading to blurry results on the
depth boundaries, especially at large upscaling factors. Given that edges represent the most …

Nowadays color-guided Depth map Super-Resolution (DSR) methods mainly have three
thorny problems:(1) joint DSR methods have serious detail and structure loss at very high …

Accurate and high-quality depth maps are required in lots of 3D applications, such as multi-
view rendering, 3D reconstruction and 3DTV. However, the resolution of captured depth …

Three-dimensional imaging using Time-of-flight (ToF) sensors is rapidly gaining widespread
adoption in many applications due to their cost effectiveness, simplicity, and compact size …