High-quality 3D reconstruction is an important topic in computer graphics and computer
vision with many applications, such as robotics and augmented reality. The advent of …

In this paper, we propose a deep learning architecture that produces accurate dense depth
for the outdoor scene from a single color image and a sparse depth. Inspired by the indoor …

The goal of our work is to complete the depth channel of an RGB-D image. Commodity-
grade depth cameras often fail to sense depth for shiny, bright, transparent, and distant …

Depth completion aims to recover dense depth maps from sparse depth measurements. It is
of increasing importance for autonomous driving and draws increasing attention from the …

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 …

Guided depth super-resolution (GDSR) is an essential topic in multi-modal image
processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones …

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 …

One of the most challenging issues in color guided depth map restoration is the
inconsistency between color edges in guidance color images and depth discontinuities on …