학술논문
End-to-End Learning of Geometry and Context for Deep Stereo Regression
Document Type
Conference
Source
2017 IEEE International Conference on Computer Vision (ICCV) ICCV Computer Vision (ICCV), 2017 IEEE International Conference on. :66-75 Oct, 2017
Subject
Language
ISSN
2380-7504
Abstract
We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images. We leverage knowledge of the problem’s geometry to form a cost volume using deep feature representations. We learn to incorporate contextual information using 3-D convolutions over this volume. Disparity values are regressed from the cost volume using a proposed differentiable soft argmin operation, which allows us to train our method end-to-end to sub-pixel accuracy without any additional post-processing or regularization. We evaluate our method on the Scene Flow and KITTI datasets and on KITTI we set a new stateof-the-art benchmark, while being significantly faster than competing approaches.