Enhancing Stereo Matching with Data Augmentation and Erase Transform

In this article, we propose a new approach to stereo matching called StereoBase, which sets a new benchmark for future exploitations in the field. Our goal is to create a simple yet powerful baseline model that can match or even surpass existing standards in performance. To achieve this, we carefully curate a data preprocessing step using techniques like RandomCrop and Erase, and then build an encoder-decoder network using 2D CNN methods. However, these methods still face challenges in achieving both high accuracy and time efficiency, so we propose additional techniques like GCNet, PSMNet, GANet, DSMNet, CoEx, and IGEV-Stereo to enhance performance.
To understand StereoBase, imagine a puzzle with many pieces that need to fit together perfectly. The pieces represent the different aspects of stereo matching, such as accurately estimating the disparity between two images. Our approach is like a magic hammer that helps these pieces fit together seamlessly, resulting in a more accurate and efficient solution.
One of the key innovations of StereoBase is the use of a "domain normalization" method to overcome challenges in cross-domain generalization. This involves creating a special volume that helps the cost volume and attention concatenation volume work together more effectively. Think of it like two different languages that need to be translated into one common language for better communication. By using this approach, we can improve performance in regions with ambiguity.
Another important aspect of StereoBase is the use of a "semi-global aggregation layer" and a "local guided aggregation layer." These layers help supplant traditional 3D convolutional neural networks with more efficient and accurate methods. Imagine these layers as two different kinds of tools that work together to build a stronger framework for stereo matching.
Finally, StereoBase also incorporates a novel approach called "Guided Cost volume Excitation" (GCE), which leverages image guidance to construct a simplified channel excitation of the cost volume. This helps improve performance in regions with ambiguity. Think of it like having a navigator who helps guide the car through unfamiliar territory, ensuring that we reach our destination more efficiently and accurately.
In summary, StereoBase is a powerful pipeline for CNN-based stereo matching that sets a new benchmark for future exploitations in the field. By carefully curating a data preprocessing step, using additional techniques like GCNet, PSMNet, GANet, DSMNet, CoEx, and IGEV-Stereo, leveraging domain normalization, and incorporating novel approaches like GCE, StereoBase is able to provide more accurate and efficient stereo matching results.