Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.00373 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916186324729856 |
|---|---|
| author | Li, Pengzhi Ding, Yikang Wang, Haohan Tang, Chengshuai Li, Zhiheng |
| author_facet | Li, Pengzhi Ding, Yikang Wang, Haohan Tang, Chengshuai Li, Zhiheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00373 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Devil is in the Edges: Monocular Depth Estimation with Edge-aware Consistency Fusion Li, Pengzhi Ding, Yikang Wang, Haohan Tang, Chengshuai Li, Zhiheng Computer Vision and Pattern Recognition 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. |
| title | The Devil is in the Edges: Monocular Depth Estimation with Edge-aware Consistency Fusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.00373 |