Stereopsis via deep learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network (2014, background): “Machine learning techniques have also been applied in the stereo case, often obtaining better results while relaxing the need for careful camera alignment [8, 13, 21, 19].”
Deep Stereo: Learning to Predict New Views from the World's Imagery (2015, methodology): “Learning has been used in several ways, including estimating the parameters of more traditional models such as MRFs [36] and learning low-level correlation filters for disparity estimation [24, 17].”
Deeper Depth Prediction with Fully Convolutional Residual Networks (2016, methodology): “Depth estimation from image data has originally relied on stereo vision [22, 32], using image pairs of the same scene to reconstruct 3D shapes.”
segDeepM: Exploiting segmentation and context in deep neural networks for object detection (2015, neutral): “They have been applied to a variety of general vision problems, such as recognition [15, 9], segmentation [11], stereo [18], flow [24], and even text-from-image generation [13], consistently outperforming past work.”
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture (2014, unknown): “However, ConvNets have recently been applied to a variety of other tasks, including pose estimation [36, 27], stereo depth [38, 25], and instance segmentation [14].”