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 …

We consider the problem of dense depth prediction from a sparse set of depth
measurements and a single RGB image. Since depth estimation from monocular images …

This paper addresses the problem of estimating the depth map of a scene given a single
RGB image. We propose a fully convolutional architecture, encompassing residual learning …

Supervised deep learning often suffers from the lack of sufficient training data. Specifically in
the context of monocular depth map prediction, it is barely possible to determine dense …

Depth sensing is a critical function for robotic tasks such as localization, mapping and
obstacle detection. There has been a significant and growing interest in depth estimation …

Deep networks have recently enjoyed enormous success when applied to recognition and
classification problems in computer vision [22, 32], but their use in graphics problems has …

Predicting the depth (or surface normal) of a scene from single monocular color images is a
challenging task. This paper tackles this challenging and essentially under-determined …

In this paper, we tackle the problem of depth completion from RGBD data. Towards this goal,
we design a simple yet effective neural network block that learns to extract joint 2D and 3D …

In this paper, we tackle the problem of estimating the depth of a scene from a single image.
This is a challenging task, since a single image on its own does not provide any depth cue …

Previous monocular depth estimation methods take a single view and directly regress the
expected results. Though recent advances are made by applying geometrically inspired loss …