Abstract

A fuzzy mask applied to pixel-wise dissimilarity weighting is proposed to improve the monocular depth estimation in this study. The parameters in the monocular depth estimation model are learned unsupervised through the image reconstruction of binocular images. The significant reconstructed dissimilarity, which is challenging to reduce, always occurs at pixels outside the binocular overlap. The fuzzy mask is designed based on the binocular overlap to adjust the weight of the dissimilarity for each pixel. More than 68% of pixels with significant dissimilarity outside binocular overlap are suppressed with weights less than 0.5. The model with the proposed fuzzy mask would focus on learning the depth estimation for pixels within binocular overlap. Experiments on the KITTI dataset show that the inference of the fuzzy mask only increases the training time of the model by less than 1%, while the number of pixels whose depth is accurately estimated enhances, and the monocular depth estimation also improves.

中文翻译：

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应用模糊蒙版改进单眼深度估计

摘要

在本研究中，提出了一种应用于像素级相异加权的模糊掩模来改进单目深度估计。单目深度估计模型中的参数是通过双目图像的图像重建无监督地学习的。显着的重建差异总是发生在双眼重叠之外的像素处，而这种差异很难减少。模糊掩模是基于双目重叠来设计的，以调整每个像素的相异性权重。超过 68% 的双目重叠之外具有显着差异的像素被小于 0.5 的权重抑制。具有所提出的模糊掩模的模型将专注于学习双目重叠内像素的深度估计。在KITTI数据集上的实验表明，模糊掩模的推理仅使模型的训练时间增加了不到1%，而准确估计深度的像素数量有所增加，单目深度估计也有所改善。