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The Devil is in the Edges: Monocular Depth Estimation with Edge-aware Consistency Fusion
Computer Vision and Pattern Recognition · Submitted 30 Mar 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Extensive experimental results on public datasets and ablation studies indicate that our method achieves state-of-the-art performance.
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The abstract, sentence by sentence
This paper presents a novel monocular depth estimation method, named ECFNet, for estimating high-quality monocular depth with clear edges and valid overall structure from a single RGB image. We make a thorough inquiry about the key factor that affects the edge depth estimation of the MDE networks, and come to a ratiocination that the edge information itself plays a critical role in predicting depth details. Driven by this analysis, we propose to explicitly employ the image edges as input for ECFNet and fuse the initial depths from different sources to produce the final depth. Specifically, ECFNet first uses a hybrid edge detection strategy to get the edge map and edge-highlighted image from the input image, and then leverages a pre-trained MDE network to infer the initial depths of the aforementioned three images. After that, ECFNet utilizes a layered fusion module (LFM) to fuse the initial depth, which will be further updated by a depth consistency module (DCM) to form the final estimation. Extensive experimental results on public datasets and ablation studies indicate that our method achieves state-of-the-art performance. Project page: https://zrealli.github.io/edgedepth.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | This paper presents a novel monocular depth estimation method, named ECFNet, for estimating high-quality monocular depth with clear edges and valid overall structure from a single RGB image. | contribution 1.00 | 0.86 | 0.15 | asserted 1.00 |
| s1 | We make a thorough inquiry about the key factor that affects the edge depth estimation of the MDE networks, and come to a ratiocination that the edge information itself plays a critical role in predicting depth details. | contribution 0.39 | 0.92 | 0.39 | asserted 1.00 |
| s2 | Driven by this analysis, we propose to explicitly employ the image edges as input for ECFNet and fuse the initial depths from different sources to produce the final depth. | contribution 0.58 | 0.87 | 0.12 | asserted 0.97 |
| s3 | Specifically, ECFNet first uses a hybrid edge detection strategy to get the edge map and edge-highlighted image from the input image, and then leverages a pre-trained MDE network to infer the initial depths of the aforementioned three images. | method 1.00 | 0.85 | 0.04 | asserted 1.00 |
| s4 | After that, ECFNet utilizes a layered fusion module (LFM) to fuse the initial depth, which will be further updated by a depth consistency module (DCM) to form the final estimation. | method 1.00 | 0.86 | 0.04 | asserted 1.00 |
| s5 | Extensive experimental results on public datasets and ablation studies indicate that our method achieves state-of-the-art performance. | result 1.00 | 0.94 | 0.54 | hedged possibility 0.60 |
| s6 | Project page: https://zrealli.github.io/edgedepth. | future work 0.48 | 0.09 | 0.02 | unclear 0.97 |
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The paper
Original abstract
This paper presents a novel monocular depth estimation method, named ECFNet, for estimating high-quality monocular depth with clear edges and valid overall structure from a single RGB image. We make a thorough inquiry about the key factor that affects the edge depth estimation of the MDE networks, and come to a ratiocination that the edge information itself plays a critical role in predicting depth details. Driven by this analysis, we propose to explicitly employ the image edges as input for ECFNet and fuse the initial depths from different sources to produce the final depth. Specifically, ECFNet first uses a hybrid edge detection strategy to get the edge map and edge-highlighted image from the input image, and then leverages a pre-trained MDE network to infer the initial depths of the aforementioned three images. After that, ECFNet utilizes a layered fusion module (LFM) to fuse the initial depth, which will be further updated by a depth consistency module (DCM) to form the final estimation. Extensive experimental results on public datasets and ablation studies indicate that our method achieves state-of-the-art performance. Project page: https://zrealli.github.io/edgedepth.
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How the literature uses it
1 arXiv paper cite this in 1 sentence
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Uses the method or tool of 1 paper
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Furthermore, as shown in the yellow box, we utilize ControlNet's [ref] edge control conditions and stable diffusion models [ref] to generate a series of images with diverse styles from the edge map.
Adding Conditional Control to Text-to-Image Diffusion Models · Introduction · 0.98
Uses data or values from 1 paper
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We evaluate our method in three commonly used datasets, DIODE [ref] , IBims-1 [ref] , and NYU-v2 [ref] .
DIODE: A Dense Indoor and Outdoor DEpth Dataset · Datasets and Metrics · 0.93
Cites as background 1 paper
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Talker et al. [ref] introduce a depth edges loss to enforce sharp edges in the estimated depth maps.
Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth Estimation · Related Works · 0.94
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