Depth Estimation for Glossy Surfaces with Light-Field Cameras

Abstract. Light-field cameras have now become available in both consumer and industrial applications, and recent papers have demonstrated practical algorithms for depth recovery from a passive single-shot capture. However, current light-field depth estimation methods are designed for Lambertian objects and fail or degrade for glossy or specular surfaces. Because light-field cameras have an array of micro-lenses, the captured data allows modification of both focus and perspec-tive viewpoints. In this paper, we develop an iterative approach to use the benefits of light-field data to estimate and remove the specular component, improving the depth estimation. The approach enables light-field data depth estimation to sup-port both specular and diffuse scenes. We present a physically-based method that estimates one or multiple light source colors. We show our method outperforms current state-of-the-art diffuse and specular separation and depth estimation al-gorithms in multiple real world scenarios.

Efficient Stereo Matching with Decoupled Dissimilarity Measure Using Successive Weighted Summation

Developing matching algorithms from stereo image pairs to obtain correct disparity maps for 3D reconstruction has been the focus of intensive research. A constant computational complexity algorithm to calculate dissimilarity aggregation in assessing disparity based on separable successive weighted summation (SWS) among horizontal and vertical directions was proposed but still not satisfactory. This paper presents a novel method which enables decoupled dissimilarity measure in the aggregation, further improving the accuracy and robustness of stereo correspondence. The aggregated cost is also used to refine disparities based on a local curve-fitting procedure. According to our experimental results on Middlebury benchmark evaluation, the proposed approach has comparable performance when compared with the selected state-of-the-art algorithms and has the lowest mismatch rate. Besides, the refinement procedure is shown to be capable of preserving object boundaries and depth discontinuities while smoothing out disparity maps

Locally Adaptive Stereo Vision Based 3D Visual Reconstruction

abstract: Using stereo vision for 3D reconstruction and depth estimation has become a popular and promising research area as it has a simple setup with passive cameras and relatively efficient processing procedure. The work in this dissertation focuses on locally adaptive stereo vision methods and applications to different imaging setups and image scenes. Solder ball height and substrate coplanarity inspection is essential to the detection of potential connectivity issues in semi-conductor units. Current ball height and substrate coplanarity inspection tools are expensive and slow, which makes them difficult to use in a real-time manufacturing setting. In this dissertation, an automatic, stereo vision based, in-line ball height and coplanarity inspection method is presented. The proposed method includes an imaging setup together with a computer vision algorithm for reliable, in-line ball height measurement. The imaging setup and calibration, ball height estimation and substrate coplanarity calculation are presented with novel stereo vision methods. The results of the proposed method are evaluated in a measurement capability analysis (MCA) procedure and compared with the ground-truth obtained by an existing laser scanning tool and an existing confocal inspection tool. The proposed system outperforms existing inspection tools in terms of accuracy and stability. In a rectified stereo vision system, stereo matching methods can be categorized into global methods and local methods. Local stereo methods are more suitable for real-time processing purposes with competitive accuracy as compared with global methods. This work proposes a stereo matching method based on sparse locally adaptive cost aggregation. In order to reduce outlier disparity values that correspond to mis-matches, a novel sparse disparity subset selection method is proposed by assigning a significance status to candidate disparity values, and selecting the significant disparity values adaptively. An adaptive guided filtering method using the disparity subset for refined cost aggregation and disparity calculation is demonstrated. The proposed stereo matching algorithm is tested on the Middlebury and the KITTI stereo evaluation benchmark images. A performance analysis of the proposed method in terms of the I0 norm of the disparity subset is presented to demonstrate the achieved efficiency and accuracy.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Segment-based stereo matching algorithm with rectification for single-lens bi-prism stereovision system

Ph.DDOCTOR OF PHILOSOPH

Efficient Techniques for High Resolution Stereo

The purpose of stereo is extracting 3-dimensional (3D) information from 2-dimensional (2D) images, which is a fundamental problem in computer vision. In general, given a known imaging geometry the position of any 3D point observed by two or more different views can be recovered by triangulation, so 3D reconstruction task relies on figuring out the pixel’s correspondence between the reference and matching images. In general computational complexity of stereo algorithms is proportional to the image resolution (the total number of pixels) and the search space (the number of depth candidates). Hence, high resolution stereo tasks are not tractable for many existing stereo algorithms whose computational costs (including the processing time and the storage space) increase drastically with higher image resolution. The aim of this dissertation is to explore techniques aimed at improving the efficiency of high resolution stereo without any accuracy loss. The efficiency of stereo is the first focus of this dissertation. We utilize the implicit smoothness property of the local image patches and propose a general framework to reduce the search space of stereo. The accumulated matching costs (measured by the pixel similarity) are investigated to estimate the representative depths of the local patch. Then, a statistical analysis model for the search space reduction based on sequential probability ratio test is provided, and an optimal sampling scheme is proposed to find a complete and compact candidate depth set according to the structure of local regions. By integrating our optimal sampling schemes as a pre-processing stage, the performance of most existing stereo algorithms can be significantly improved. The accuracy of stereo algorithms is the second focus. We present a plane-based approach for the local geometry estimation combining with a parallel structure propagation algorithm, which outperforms most state-of-the-art stereo algorithms. To obtain precise local structures, we also address the problem of utilizing surface normals, and provide a framework to integrate color and normal information for high quality scene reconstruction.Doctor of Philosoph