基于自监督单目深度估计的自提取特征聚合

Zhengming Zhou, Qiulei Dong
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引用次数: 14

摘要

自监督单目深度估计是近年来计算机视觉领域研究的热点。现有文献大多通过直接拼接或元素相加的方式聚合多尺度特征进行深度预测,但这种特征聚合操作往往忽略了多尺度特征之间的上下文一致性。针对这一问题,我们提出了自蒸馏特征聚合(SDFA)模块,用于同时聚合一对低规模和高规模特征并保持其上下文一致性。SDFA采用三个分支分别学习三个特征偏移映射:一个偏移映射用于细化输入的低尺度特征,另外两个偏移映射用于细化输入的高尺度特征,并采用设计的自蒸馏方式。然后,我们提出了一种基于SDFA的自监督单目深度估计网络,并设计了一种自蒸馏训练策略,利用SDFA模块对所提出的网络进行训练。在KITTI数据集上的实验结果表明,该方法在大多数情况下优于比较先进的方法。代码可在https://github.com/ZM-Zhou/SDFA-Net_pytorch上获得。
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Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimation
Self-supervised monocular depth estimation has received much attention recently in computer vision. Most of the existing works in literature aggregate multi-scale features for depth prediction via either straightforward concatenation or element-wise addition, however, such feature aggregation operations generally neglect the contextual consistency between multi-scale features. Addressing this problem, we propose the Self-Distilled Feature Aggregation (SDFA) module for simultaneously aggregating a pair of low-scale and high-scale features and maintaining their contextual consistency. The SDFA employs three branches to learn three feature offset maps respectively: one offset map for refining the input low-scale feature and the other two for refining the input high-scale feature under a designed self-distillation manner. Then, we propose an SDFA-based network for self-supervised monocular depth estimation, and design a self-distilled training strategy to train the proposed network with the SDFA module. Experimental results on the KITTI dataset demonstrate that the proposed method outperforms the comparative state-of-the-art methods in most cases. The code is available at https://github.com/ZM-Zhou/SDFA-Net_pytorch.
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