Towards Minimal Focal Stack in Shape from Focus

• URL: http://arxiv.org/abs/2604.01603v1
• Date: Thu, 02 Apr 2026 04:25:48 GMT
• Title: Towards Minimal Focal Stack in Shape from Focus
• Abstract summary: Shape from Focus (SFF) is a technique that estimates scene structure from focus variations observed across a focal stack.<n>A key limitation of SFF methods is their reliance on densely sampled, large focal stacks.<n>We propose a focal stack augmentation that enables SFF methods to estimate depth using a reduced stack of just two images.
• Score: 1.7188280334580195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shape from Focus (SFF) is a depth reconstruction technique that estimates scene structure from focus variations observed across a focal stack, that is, a sequence of images captured at different focus settings. A key limitation of SFF methods is their reliance on densely sampled, large focal stacks, which limits their practical applicability. In this study, we propose a focal stack augmentation that enables SFF methods to estimate depth using a reduced stack of just two images, without sacrificing precision. We introduce a simple yet effective physics-based focal stack augmentation that enriches the stack with two auxiliary cues: an all-in-focus (AiF) image estimated from two input images, and Energy-of-Difference (EOD) maps, computed as the energy of differences between the AiF and input images. Furthermore, we propose a deep network that computes a deep focus volume from the augmented focal stacks and iteratively refines depth using convolutional Gated Recurrent Units (ConvGRUs) at multiple scales. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed augmentation benefits existing state-of-the-art SFF models, enabling them to achieve comparable accuracy. The results also show that our approach maintains state-of-the-art performance with a minimal stack size.

Related papers

• UDPNet: Unleashing Depth-based Priors for Robust Image Dehazing [77.10640210751981]
  UDPNet is a general framework that leverages depth-based priors from a large-scale pretrained depth estimation model DepthAnything V2.<n>Our proposed solution establishes a new benchmark for depth-aware dehazing across various scenarios.
  arXiv  Detail & Related papers  (2026-01-11T13:29:02Z)
• Robust Shape from Focus via Multiscale Directional Dilated Laplacian and Recurrent Network [1.7188280334580195]
  Shape-from-Focus (SFF) is a passive depth estimation technique that infers scene depth by analyzing focus variations in a focal stack.<n>We propose a hybrid framework that computes multi-scale focus volumes using Directional Dilated Laplacian kernels.<n>Our approach achieves superior accuracy and generalization across diverse focal conditions.
  arXiv  Detail & Related papers  (2025-12-11T10:19:52Z)
• DiffCamera: Arbitrary Refocusing on Images [55.948229011478304]
  We propose DiffCamera, a model that enables flexible refocusing of a created image conditioned on an arbitrary new focus point and a blur level.<n>Experiments demonstrate that DiffCamera supports stable refocusing across a wide range of scenes, providing unprecedented control over DoF adjustments for photography and generative AI applications.
  arXiv  Detail & Related papers  (2025-09-30T17:48:23Z)
• DualFocus: Depth from Focus with Spatio-Focal Dual Variational Constraints [26.266318338511876]
  We present DualFocus, a novel DFF framework that leverages the focal stack's unique gradient patterns induced by focus variation.<n>We show that DualFocus consistently outperforms state-of-the-art methods in both depth accuracy and perceptual quality.
  arXiv  Detail & Related papers  (2025-09-26T07:15:36Z)
• Towards Real-World Focus Stacking with Deep Learning [97.34754533628322]
  We introduce a new dataset consisting of 94 high-resolution bursts of raw images with focus bracketing. This dataset is used to train the first deep learning algorithm for focus stacking capable of handling bursts of sufficient length for real-world applications.
  arXiv  Detail & Related papers  (2023-11-29T17:49:33Z)
• 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)
• Learning Depth from Focus in the Wild [16.27391171541217]
  We present a convolutional neural network-based depth estimation from single focal stacks. Our method allows depth maps to be inferred in an end-to-end manner even with image alignment. For the generalization of the proposed network, we develop a simulator to realistically reproduce the features of commercial cameras.
  arXiv  Detail & Related papers  (2022-07-20T05:23:29Z)
• Deep Depth from Focus with Differential Focus Volume [17.505649653615123]
  We propose a convolutional neural network (CNN) to find the best-focused pixels in a focal stack and infer depth from the focus estimation. The key innovation of the network is the novel deep differential focus volume (DFV)
  arXiv  Detail & Related papers  (2021-12-03T04:49:51Z)
• Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision [10.547816678110417]
  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.
  arXiv  Detail & Related papers  (2021-08-24T17:09:13Z)
• A learning-based view extrapolation method for axial super-resolution [52.748944517480155]
  Axial light field resolution refers to the ability to distinguish features at different depths by refocusing. We propose a learning-based method to extrapolate novel views from axial volumes of sheared epipolar plane images.
  arXiv  Detail & Related papers  (2021-03-11T07:22:13Z)
• 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)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.