A feature extraction technique in stereo matching network
Lin Yang, Jun Zhang, Yingyun Yang · 2019
Feature extraction is the basic and vital part of stereo matching. Accurate and effective feature extraction is the precondition of correct disparity estimation. Large-scale pooling is widely used to extract high-level features in the stereo matching network, but a lot of semantics is discarded. Meanwhile, frank bilinear interpolation with rare high-level information causes error propagation in the matching cost calculating. We apply Feature Pyramid Net to extract the multi-scale features with more context information and restore resolution layer by layer. The approach is validated on KITTI and Scene Flow.