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Article overview
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Structure-Attentioned Memory Network for Monocular Depth Estimation | | Jing Zhu
; Yunxiao Shi
; Mengwei Ren
; Yi Fang
; Kuo-Chin Lien
; Junli Gu
; | | Date: |
10 Sep 2019 | | Abstract: | Monocular depth estimation is a challenging task that aims to predict a
corresponding depth map from a given single RGB image. Recent deep learning
models have been proposed to predict the depth from the image by learning the
alignment of deep features between the RGB image and the depth domains. In this
paper, we present a novel approach, named Structure-Attentioned Memory Network,
to more effectively transfer domain features for monocular depth estimation by
taking into account the common structure regularities (e.g., repetitive
structure patterns, planar surfaces, symmetries) in domain adaptation. To this
end, we introduce a new Structure-Oriented Memory (SOM) module to learn and
memorize the structure-specific information between RGB image domain and the
depth domain. More specifically, in the SOM module, we develop a Memorable Bank
of Filters (MBF) unit to learn a set of filters that memorize the
structure-aware image-depth residual pattern, and also an Attention Guided
Controller (AGC) unit to control the filter selection in the MBF given image
features queries. Given the query image feature, the trained SOM module is able
to adaptively select the best customized filters for cross-domain feature
transferring with an optimal structural disparity between image and depth. In
summary, we focus on addressing this structure-specific domain adaption
challenge by proposing a novel end-to-end multi-scale memorable network for
monocular depth estimation. The experiments show that our proposed model
demonstrates the superior performance compared to the existing supervised
monocular depth estimation approaches on the challenging KITTI and NYU Depth V2
benchmarks. | | Source: | arXiv, 1909.4594 | | Services: | Forum | Review | PDF | Favorites | |
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