Underwater optical images are usually influenced by low lighting, high turbidity scattering
and wavelength absorption. To solve these issues, a great deal of work has been performed …

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 …

Depth completion aims to derive a dense depth map from sparse depth measurements with
a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly …

multimodal image fusion involves tasks like pan-sharpening and depth super-resolution.
Both tasks aim to generate high-resolution target images by fusing the complementary …

Scene understanding is an active research area. Commercial depth sensors, such as Kinect,
have enabled the release of several RGB-D datasets over the past few years which …

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 …

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 …

Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms
to enable real-time inference. Towards this goal, we developed a differentiable PatchMatch …

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 estimation from a single image is a fundamental problem in computer vision. In this
paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) …