Depth estimation is a classic task in computer vision, which is of great significance for many
applications such as augmented reality, target tracking and autonomous driving. Traditional …

Depth information is important for autonomous systems to perceive environments and
estimate their own state. Traditional depth estimation methods, like structure from motion …

Estimating the accurate depth from a single image is challenging since it is inherently
ambiguous and ill-posed. While recent works design increasingly complicated and powerful …

Monocular depth estimation is fundamental for 3D scene understanding and downstream
applications. However, even under the supervised setup, it is still challenging and ill-posed …

We address the problem of estimating a high quality dense depth map from a single RGB
input image. We start out with a baseline encoder-decoder convolutional neural network …

Monocular depth estimation is vital for scene understanding and downstream tasks. We
focus on the supervised setup, in which ground-truth depth is available only at training time …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

While convolutional neural networks have shown a tremendous impact on various computer
vision tasks, they generally demonstrate limitations in explicitly modeling long-range …

Depth estimation and scene parsing are two particularly important tasks in visual scene
understanding. In this paper we tackle the problem of simultaneous depth estimation and …

In this paper, we propose a novel Pattern-Affinitive Propagation (PAP) framework to jointly
predict depth, surface normal and semantic segmentation. The motivation behind it comes …