Cost aggregation is crucial to the accuracy of stereo matching. A reasonable cost algorithm should aggregate costs within homogeneous regions where pixels have same or similar disparities. Otherwise, estimated disparity map prone well-known edge-fattening issue and problem losing fine structures. However, current state-of-the-art convolutional neural networks (CNNs) mainly do with square-kernel...

Abstract Neural networks are becoming more popular than traditional methods in stereo matching. The can be decomposed into four sub-modules: feature extraction / matching cost computation, aggregation, disparity computation optimization, and refinement. A typical design for the is that left right branches share same weights. However, Siamese weak at distinguishing neighboring patches because of...