Abstract 3D object detection is an essential task in autonomous driving. Recent techniques
excel with highly accurate detection rates, provided the 3D input data is obtained from …

With a great success of the generative model via deep neural networks, monocular depth
estimation has been actively studied by exploiting various encoder-decoder architectures …

Single-view depth prediction is a fundamental problem in computer vision. Recently, deep
learning methods have led to significant progress, but such methods are limited by the …

Learning to predict scene depth from RGB inputs is a challenging task both for indoor and
outdoor robot navigation. In this work we address unsupervised learning of scene depth and …

There are two major types of uncertainty one can model. Aleatoric uncertainty captures
noise inherent in the observations. On the other hand, epistemic uncertainty accounts for …

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, map** and
obstacle detection. There has been a significant and growing interest in depth estimation …