Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Error Diagnosis of Deep Monocular Depth Estimation Models   Estimating depth from a monocular image is an ill-posed problem: when the
camera projects a 3D scene onto a 2D plane, depth information is inherently and
permanently lost. Nevertheless, recent work has shown impressive results in
estimating 3D structure from 2D images using deep learning. In this paper, we
put on an introspective hat and analyze state-of-the-art monocular depth
estimation models in indoor scenes to understand these models' limitations and
error patterns. To address errors in depth estimation, we introduce a novel
Depth Error Detection Network (DEDN) that spatially identifies erroneous depth
predictions in the monocular depth estimation models. By experimenting with
multiple state-of-the-art monocular indoor depth estimation models on multiple
datasets, we show that our proposed depth error detection network can identify
a significant number of errors in the predicted depth maps. Our module is
flexible and can be readily plugged into any monocular depth prediction network
to help diagnose its results. Additionally, we propose a simple yet effective
Depth Error Correction Network (DECN) that iteratively corrects errors based on
our initial error diagnosis.

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The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: nlin
21
The categories of this paper are: adap-org
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The categories of this paper are: ao-sci
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: math