For calculating 3D information with stereo matching, usually correspondence analysis yields a so-called depth hypotheses cost stack, which contains information about similarities of the visible structures at all positions of the analyzed stereo images. Often those cost values comprise a large amount of noise and/or ambiguities, so that regularization is required. The Conditional Random Field (CRF) regularizer from Shekhovtsov et al. [Sh16] is a very good algorithm among various methods. Due to the usual iterative nature of those regularizers, they often do not meet the strict speed and memory requirements posed in many real-world applications. In this paper, we propose to substitute Shekhovtsov’s CRF algorithm with an especially designed U-shaped 3D Convolutional Neural Network (3D-CRF-CNN), which is taught proper regularization by the CRF algorithm as a teacher. Our experiments have shown, that such a 3D-CRF-CNN is not only able to mimic the CRF’s regularizing behavior, but - if properly setup - also comprises remarkable generalization capabilities compared to a state-of-the-art 2D-CNN that is trained on a slightly different, yet equivalent, task. The advantages of such a CNN regularizer are its predictable computational performance and its relatively simple architectural structure, which allows for easy development, speed up, and deployment. We demonstrate the feasibility of the concept of training a 3D-CRF-CNN to take over CRF’s regularizing functionality on the basis of available test data and show that it pays off to invest special effort into tailoring an according CNN architecture.
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- Deep Learning as Substitute for CRF Regularization in 3D Image Processing
- Springer Berlin Heidelberg