Robust Shape from Focus via Multiscale Directional Dilated Laplacian and Recurrent Network

• URL: http://arxiv.org/abs/2512.10498v1
• Date: Thu, 11 Dec 2025 10:19:52 GMT
• Title: Robust Shape from Focus via Multiscale Directional Dilated Laplacian and Recurrent Network
• Authors: Khurram Ashfaq, Muhammad Tariq Mahmood,
• Abstract summary: 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.
• Score: 1.7188280334580195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shape-from-Focus (SFF) is a passive depth estimation technique that infers scene depth by analyzing focus variations in a focal stack. Most recent deep learning-based SFF methods typically operate in two stages: first, they extract focus volumes (a per pixel representation of focus likelihood across the focal stack) using heavy feature encoders; then, they estimate depth via a simple one-step aggregation technique that often introduces artifacts and amplifies noise in the depth map. To address these issues, we propose a hybrid framework. Our method computes multi-scale focus volumes traditionally using handcrafted Directional Dilated Laplacian (DDL) kernels, which capture long-range and directional focus variations to form robust focus volumes. These focus volumes are then fed into a lightweight, multi-scale GRU-based depth extraction module that iteratively refines an initial depth estimate at a lower resolution for computational efficiency. Finally, a learned convex upsampling module within our recurrent network reconstructs high-resolution depth maps while preserving fine scene details and sharp boundaries. Extensive experiments on both synthetic and real-world datasets demonstrate that our approach outperforms state-of-the-art deep learning and traditional methods, achieving superior accuracy and generalization across diverse focal conditions.

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