Abstract
Estimating the 3D shape of a scene from differently focused set of images has been a practical approach for 3D reconstruction with color cameras. However, reconstructed depth with existing depth from focus (DFF) methods still suffer from poor quality with textureless and object boundary regions. In this paper, we propose an improved depth estimation based on depth from focus iteratively refining 3D shape from uniformly focused image set (UFIS). We investigated the appearance changes in spatial and frequency domains in iterative manner. In order to achieve sub-frame accuracy in depth estimation, optimal location of focused frame in DFF is estimated by fitting a polynomial curve on the dissimilarity measurements. In order to avoid wrong depth values on texture-less regions we propose to build a confidence map and use it to identify erroneous depth estimations. We evaluated our method on public and our own datasets obtained from different types of devices, such as smartphones, medical, and normal color cameras. Quantitative and qualitative evaluations on various test image sets show promising performance of the proposed method in depth estimation.
Highlights
Estimating three dimensional shape of a scene from color image is a challenging task [1].Without any prior knowledge on the scene, it is an ill-posed problem to recover three dimensional shape of objects using single color camera
Depth from focus (DFF), or shape from focus (SFF), is a technique for depth estimation from a set of image frames having continuously changing focus amount that are taken at the same location and viewpoint
Our main contributions are the proposal of a new focus measure that uses both spatial and frequency domain information, creating a uniformly focused image set (UFIS) to achieve more accurate depth, as well as hole-filling process that fixes erroneous depth values caused by textureless region
Summary
Estimating three dimensional shape of a scene from color image is a challenging task [1]. In Reference [19], the authors propose a new focus measurement that is robust to noise with higher accuracy in focus measurement They present Ring Difference filter (RDF) by inserting a gap and looking at the pixels that are located farther away from the point of interest (POI). Zhiqiang et al [21] proposed depth recovery framework including depth reconstruction and refinement process They use non-local matting Laplacian prior and variance based confidence level computation. Our main contributions are the proposal of a new focus measure that uses both spatial and frequency domain information, creating a uniformly focused image set (UFIS) to achieve more accurate depth, as well as hole-filling process that fixes erroneous depth values caused by textureless region.
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