A 3D object–based depth estimation approach from stereoscopic image sequences is presented in this paper that introduces a memory for depth information from preceding image pairs. This approach is used in the EU–RACE project DISTIMA for applications like remote handling, quality control and TV post processing. Compared to depth estimation approaches that compute depth maps from consecutive ster...

The finite depth of field of a real camera can be used to estimate the depth structure of a scene. While the distance of an object from the plane in focus determines the defocus blur size, the shape of the aperture determines the shape of the blur. This blur shape can be manipulated by introducing masks into the main lens aperture. We propose an intuitive criterion to design aperture patterns f...

Monocular depth estimation is a challenging task in complex compositions depicting multiple objects of diverse scales. Albeit the recent great progress thanks to the deep convolutional neural networks (CNNs), the state-of-the-art monocular depth estimation methods still fall short to handle such real-world challenging scenarios. In this paper, we propose a deep end-to-end learning framework to ...

In this paper we consider the problem of estimating depth maps from multiple views within a variational framework. Previous work has demonstrated that multiple views improve the depth reconstruction, and that higher order regularisers model a good prior for typical realworld 3D scenes. We build on these findings and stress an important aspect that has not been considered in variational multivie...

We present a method for jointly predicting a depth map and intrinsic images from single-image input. The two tasks are formulated in a synergistic manner through a joint conditional random field (CRF) that is solved using a novel convolutional neural network (CNN) architecture, called the joint convolutional neural field (JCNF) model. Tailored to our joint estimation problem, JCNF differs from ...

Depth map estimation is an active and long standing problem in image/video processing and computer vision. Conventional depth estimation algorithms which rely on stereo/multi-view vision or depth sensing devices alone are limited by complicated scenes or imperfections of the depth sensing devices. On the other hand, the depth maps obtained from the stereo/multi-view vision and depth sensing dev...

In this paper we consider the problem of single monocular image depth estimation. It is a challenging problem due to its ill-posedness nature and has found wide application in industry. Previous efforts belongs roughly to two families: learning-based method and interactive method. Learning-based method, in which deep convolutional neural network (CNN) is widely used, can achieve good result. Bu...

there are various approaches for depth estimation of anomalous potential field data. spectral analysis of gravity and magnetic data has been used extensively for many years to derive the depth to certain geological structures, such as the magnetic basement or the curie temperature isotherm. the interpretation of the gravity and magnetic data is preferred in frequency domain because of simple re...

Self-supervised monocular depth estimation methods typically rely on the reprojection error to capture geometric relationships between successive frames in static environments. However, this assumption does not hold dynamic objects scenarios, leading errors during view synthesis stage, such as feature mismatch and occlusion, which can significantly reduce accuracy of generated maps. To address ...