Error Diagnosis of Deep Monocular Depth Estimation Models
- URL: http://arxiv.org/abs/2112.05533v1
- Date: Mon, 15 Nov 2021 22:13:28 GMT
- Title: Error Diagnosis of Deep Monocular Depth Estimation Models
- Authors: Jagpreet Chawla, Nikhil Thakurdesai, Anuj Godase, Md Reza, David
Crandall and Soon-Heung Jung
- Abstract summary: We 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.
Our module is flexible and can be readily plugged into any monocular depth prediction network to help diagnose its results.
- Score: 0.2770822269241973
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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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