Related papers: Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision

Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision

URL: http://arxiv.org/abs/2108.10843v1
Date: Tue, 24 Aug 2021 17:09:13 GMT
Title: Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision
Authors: Ning-Hsu Wang, Ren Wang, Yu-Lun Liu, Yu-Hao Huang, Yu-Lin Chang, Chia-Ping Chen and Kevin Jou
Abstract summary: The proposed method can be trained either supervisedly with ground truth depth, or emphunsupervisedly with AiF images as supervisory signals. We show in various experiments that our method outperforms the state-of-the-art methods both quantitatively and qualitatively.
Score: 10.547816678110417
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Depth estimation is a long-lasting yet important task in computer vision. Most of the previous works try to estimate depth from input images and assume images are all-in-focus (AiF), which is less common in real-world applications. On the other hand, a few works take defocus blur into account and consider it as another cue for depth estimation. In this paper, we propose a method to estimate not only a depth map but an AiF image from a set of images with different focus positions (known as a focal stack). We design a shared architecture to exploit the relationship between depth and AiF estimation. As a result, the proposed method can be trained either supervisedly with ground truth depth, or \emph{unsupervisedly} with AiF images as supervisory signals. We show in various experiments that our method outperforms the state-of-the-art methods both quantitatively and qualitatively, and also has higher efficiency in inference time.

Related papers

Blur aware metric depth estimation with multi-focus plenoptic cameras [8.508198765617196]
We present a new metric depth estimation algorithm using only raw images from a multi-focus plenoptic camera. The proposed approach is especially suited for the multi-focus configuration where several micro-lenses with different focal lengths are used.
arXiv Detail & Related papers (2023-08-08T13:38:50Z)
FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene [57.26600120397529]
It has long been an ill-posed problem to predict absolute depth maps from single images in real (unseen) indoor scenes. We develop a focal-and-scale depth estimation model to well learn absolute depth maps from single images in unseen indoor scenes.
arXiv Detail & Related papers (2023-07-27T04:49:36Z)
Depth and DOF Cues Make A Better Defocus Blur Detector [27.33757097343283]
Defocus blur detection (DBD) separates in-focus and out-of-focus regions in an image. Previous approaches mistakenly mistook homogeneous areas in focus for defocus blur regions. We propose an approach called D-DFFNet, which incorporates depth and DOF cues in an implicit manner.
arXiv Detail & Related papers (2023-06-20T07:03:37Z)
Fully Self-Supervised Depth Estimation from Defocus Clue [79.63579768496159]
We propose a self-supervised framework that estimates depth purely from a sparse focal stack. We show that our framework circumvents the needs for the depth and AIF image ground-truth, and receives superior predictions.
arXiv Detail & Related papers (2023-03-19T19:59:48Z)
Multi-task Learning for Monocular Depth and Defocus Estimations with Real Images [3.682618267671887]
Most existing methods treat depth estimation and defocus estimation as two separate tasks, ignoring the strong connection between them. We propose a multi-task learning network consisting of an encoder with two decoders to estimate the depth and defocus map from a single focused image. Our depth and defocus estimations achieve significantly better performance than other state-of-art algorithms.
arXiv Detail & Related papers (2022-08-21T08:59:56Z)
Deep Depth from Focal Stack with Defocus Model for Camera-Setting Invariance [19.460887007137607]
We propose a learning-based depth from focus/defocus (DFF) which takes a focal stack as input for estimating scene depth. We show that our method is robust against a synthetic-to-real domain gap, and exhibits state-of-the-art performance.
arXiv Detail & Related papers (2022-02-26T04:21:08Z)
Single image deep defocus estimation and its applications [82.93345261434943]
We train a deep neural network to classify image patches into one of the 20 levels of blurriness. The trained model is used to determine the patch blurriness which is then refined by applying an iterative weighted guided filter. The result is a defocus map that carries the information of the degree of blurriness for each pixel.
arXiv Detail & Related papers (2021-07-30T06:18:16Z)
Defocus Blur Detection via Depth Distillation [64.78779830554731]
We introduce depth information into DBD for the first time. In detail, we learn the defocus blur from ground truth and the depth distilled from a well-trained depth estimation network. Our approach outperforms 11 other state-of-the-art methods on two popular datasets.
arXiv Detail & Related papers (2020-07-16T04:58:09Z)
Single Image Depth Estimation Trained via Depth from Defocus Cues [105.67073923825842]
Estimating depth from a single RGB image is a fundamental task in computer vision. In this work, we rely, instead of different views, on depth from focus cues. We present results that are on par with supervised methods on KITTI and Make3D datasets and outperform unsupervised learning approaches.
arXiv Detail & Related papers (2020-01-14T20:22:54Z)
Video Depth Estimation by Fusing Flow-to-Depth Proposals [65.24533384679657]
We present an approach with a differentiable flow-to-depth layer for video depth estimation. The model consists of a flow-to-depth layer, a camera pose refinement module, and a depth fusion network. Our approach outperforms state-of-the-art depth estimation methods, and has reasonable cross dataset generalization capability.
arXiv Detail & Related papers (2019-12-30T10:45:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.