A Light in Dark Places xe2x80x93 3D Reconstruction from Stereo Views with a Moving Light Source In this paper we consider the problem of recovering depth from a moving camera with an attached light. The fact that the light is moving means that the appearance of the scene changes, making intensitybased correlation difficult. These appearance changes, however, are governed by the motion of the camera, and are intimately related to the depth of the scene. This allows us to derive constraints on the intensities of corresponding points. We show that, in principle, this approach allows us to recover depth and surface albedo (up to a scale from light intensity) from two calibrated views. This is possible even when viewing textureless surfaces, which are intractable for traditional stereo methods. However, the method is very sensitive to quantisation of intensity values, and so is not practical on its own for normal camera images. The underlying constraints, however, may be useful as part of larger systems.

A 3D Convolutional Neural Network for Light Field Depth Estimation Depth estimation has always been a great challenge in the field of computer vision and machine learning. There is a rich literature focusing on depth estimation in stereo vision or in monocular imaging, while the domain of depth estimation in light field images is still in its infancy. The paper proposes a fully convolutional 3D neural network that estimates the disparity in light field images. The proposed method is parametric as it is able to adapt to input images of arbitrary size and it is lightweight and less prone to overfitting thanks to its fully convolutional nature. The experiments reveal competitive results against the state of the art, demonstrating the potential offered by deep learning solutions for disparity estimation in light field images.