Depth estimation is a key problem in 3D computer vision and has a wide variety of applications. In this paper we explore whether deep learning network can predict depth map accurately by learning multi-scale spatio-temporal features from sequences and recasting the depth estimation from a regression task to an ordinal classification task. We design an encoder-decoder network with several multi-scale strategies to improve its performance and extract spatio-temporal features with ConvLSTM. The results of our experiments show that the proposed method has an improvement of almost 10% in error metrics and up to 2% in accuracy metrics. The results also tell us that extracting spatio-temporal features can dramatically improve the performance in depth estimation task. We consider to extend this work to a self-supervised manner to get rid of the dependence on large-scale labeled data.

中文翻译：

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通过顺序分类从序列中进行多尺度时空特征提取和深度估计。

深度估计是3D计算机视觉中的关键问题，具有广泛的应用。在本文中，我们探索了深度学习网络是否可以通过从序列中学习多尺度时空特征并将深度估计值从回归任务转换为顺序分类任务来准确预测深度图。我们设计了一种具有多种多尺度策略的编解码器网络，以改善其性能并使用ConvLSTM提取时空特征。我们的实验结果表明，所提出的方法在误差指标上提高了近10％，在准确性指标上提高了2％。结果还告诉我们，提取时空特征可以极大地提高深度估计任务的性能。