Abstract
In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We propose two novel neural net layers, aimed at capturing local and the whole-image cost dependencies respectively. The first is a semi-global aggregation layer which is a differentiable approximation of the semi-global matching, the second is the local guided aggregation layer which follows a traditional cost filtering strategy to refine thin structures. These two layers can be used to replace the widely used 3D convolutional layer which is computationally costly and memory-consuming as it has cubic computational/memory complexity. In the experiments, we show that nets with a two-layer guided aggregation block easily outperform the state-of-the-art GC-Net which has nineteen 3D convolutional layers. We also train a deep guided aggregation network (GA-Net) which gets better accuracies than state-of-the-art methods on both Scene Flow dataset and KITTI benchmarks.
Abstract (translated)
在立体匹配任务中，匹配成本的聚集在传统方法和深度神经网络模型中都起着至关重要的作用，以准确估计视差。我们提出了两种新的神经网络层，分别用于捕获局部和整个图像的成本依赖性。第一种是半全局聚集层，它是半全局匹配的可微近似；第二种是局部引导聚集层，它遵循传统的成本过滤策略来细化细结构。这两层可以用来取代广泛使用的三维卷积层，因为它具有立方计算/内存复杂性，计算成本高，内存消耗大。在实验中，我们发现具有两层导向聚合块的网络很容易优于具有十九个三维卷积层的最先进的GC网络。我们还培训了一个深度引导聚合网络（GA NET），它在场景流数据集和Kitti基准上的精确度比最先进的方法高。
URL
https://arxiv.org/abs/1904.06587
