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Dense 3D Reconstruction from Specularity Consistency
"... In this work, we consider the dense reconstruction of specular objects. We propose the use of a specularity constraint, based on surface normal/depth consistency, to define a matching cost function that can drive standard stereo reconstruction methods. We discuss the types of ambiguity that can aris ..."
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In this work, we consider the dense reconstruction of specular objects. We propose the use of a specularity constraint, based on surface normal/depth consistency, to define a matching cost function that can drive standard stereo reconstruction methods. We discuss the types of ambiguity that can arise, and suggest an aggregation method based on anisotropic diffusion that is particularly suitable for this matching cost function. We also present a controlled illumination setup that includes a pair of cameras and one LCD monitor, which is used as a calibrated, variable-position light source. We use this setup to evaluate the proposed method on real data, and demonstrate its capacity to recover high-quality depth and orientation from specular objects. 1.
Improving Stereo Sub-Pixel Accuracy for Long Range Stereo
"... Dense stereo algorithms are able to estimate disparities at all pixels including untextured regions. Typically these disparities are evaluated at integer disparity steps. A subsequent sub-pixel interpolation often fails to propagate smoothness constraints on a sub-pixel level. The determination of s ..."
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Dense stereo algorithms are able to estimate disparities at all pixels including untextured regions. Typically these disparities are evaluated at integer disparity steps. A subsequent sub-pixel interpolation often fails to propagate smoothness constraints on a sub-pixel level. The determination of sub-pixel accurate disparities is an active field of research, however, most sub-pixel estimation algorithms focus on textured image areas in order to show their precision. We propose to increase the sub-pixel accuracy in lowtextured regions in three possible ways: First, we present an analysis that shows the benefit of evaluating the disparity space at fractional disparities. Second, we introduce a new disparity smoothing algorithm that preserves depth discontinuities and enforces smoothness on a sub-pixel level. Third, we present a novel stereo constraint (gravitational constraint) that assumes sorted disparity values in vertical direction and guides global algorithms to reduce false matches, especially in low-textured regions. Our goal in this work is to obtain an accurate 3D reconstruction. Largescale 3D reconstruction will benefit heavily from these subpixel refinements, especially with a multi-baseline extension. Results based on semi-global matching, obtained with the above mentioned algorithmic extensions are shown for the Middlebury stereo ground truth data sets. The presented improvements, called ImproveSubPix, turn out to be one of the top-performing algorithms when evaluating the set on a sub-pixel level while being computationally efficient. Additional results are presented for urban scenes. The three improvements are independent of the underlying type of stereo algorithm and can also be applied to sparse stereo algorithms.
3d lunar terrain reconstruction from apollo images
- In ISVC
, 2009
"... Abstract. Generating accurate three dimensional planetary models is becoming increasingly important as NASA plans manned missions to return to the Moon in the next decade. This paper describes a 3D surface reconstruction system called the Ames Stereo Pipeline that is designed to produce such models ..."
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Abstract. Generating accurate three dimensional planetary models is becoming increasingly important as NASA plans manned missions to return to the Moon in the next decade. This paper describes a 3D surface reconstruction system called the Ames Stereo Pipeline that is designed to produce such models automatically by processing orbital stereo imagery. We discuss two important core aspects of this system: (1) refinement of satellite station positions and pose estimates through least squares bundle adjustment; and (2) a stochastic plane fitting algorithm that generalizes the Lucas-Kanade method for optimal matching between stereo pair images.. These techniques allow us to automatically produce seamless, highly accurate digital elevation models from multiple stereo image pairs while significantly reducing the influence of image noise. Our technique is demonstrated on a set of 71 high resolution scanned images from the Apollo 15 mission. 1
A BAYESIAN FORMULATION FOR SUB-PIXEL REFINEMENT IN STEREO ORBITAL IMAGERY
"... Generating accurate three dimensional planetary models is becoming increasingly more important as NASA plans manned missions to return to the moon in the next decade. This paper describes a stereo correspondence system for orbital images and focuses on a novel approach for the sub-pixel refinement o ..."
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Generating accurate three dimensional planetary models is becoming increasingly more important as NASA plans manned missions to return to the moon in the next decade. This paper describes a stereo correspondence system for orbital images and focuses on a novel approach for the sub-pixel refinement of the disparity maps. Our method uses a Bayesian formulation that generalizes the Lucas-Kanade method for optimal matching between stereo pair images. This approach reduces significantly the pixel locking effect of the earlier methods and reduces the influence of image noise. The method is demonstrated on a set of high resolution scanned images from the Apollo era missions. approaches relying on Lucas Kanade algorithm [1] proposes an asymmetric score where the disparity map is computed using the best matching score between the left image block and an optimally affine transformed block in the right image. Recently, several statistical approaches [2] have emerged to show encouraging results. Our sub-pixel refinement approach generalizes the earlier work by Stein et al. [8] to a Bayesian framework that models both the data and image noise. Iteratively estimating the model parameters determines the optimal disparity map that reduces the effects of image noise and attenuates the sub-pixel locking effect. The next 1.
How accurate can block matches be in stereo vision
- SIAM Journal on Imaging Sciences
, 2011
"... Abstract. This article explores the sub-pixel accuracy attainable for the disparity computed from a rectified stereo pair of images with small baseline. In this framework we consider translations as the local deformation model between patches in the images. A mathematical study shows first how discr ..."
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Abstract. This article explores the sub-pixel accuracy attainable for the disparity computed from a rectified stereo pair of images with small baseline. In this framework we consider translations as the local deformation model between patches in the images. A mathematical study shows first how discrete block-matching can be performed with arbitrary precision under Shannon-Whittaker conditions. This study leads to the specification of a block-matching algorithm which is able to refine disparities with sub-pixel accuracy. Moreover, a formula for the variance of the disparity error caused by the noise is introduced and proved. Several simulated and real experiments show a decent agreement between this theoretical error variance and the observed RMSE in stereo pairs with good SNR and low baseline. A practical consequence is that under realistic sampling and noise conditions in optical imaging, the disparity map in stereo-rectified images can be computed for the majority of pixels (but only for those pixels with meaningful matches) with a 1/20 pixel precision. Key words. Block-matching, sub-pixel accuracy, noise error estimate. 1. Introduction. Stereo algorithms aim at reconstructing a 3D model from two or more images of the same scene acquired from different angles. Assuming for a sake of simplicity that the cameras are calibrated, and that the image pair has been
Advances in 3D Shape Acquisition
"... In this dissertation we discuss a variety of techniques that advance the state of the art in the field of 3D shape acquisition from real world objects. The research was done in collaboration with ..."
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In this dissertation we discuss a variety of techniques that advance the state of the art in the field of 3D shape acquisition from real world objects. The research was done in collaboration with
Computer Vision and Image Understanding 116 (2012) 250–261 Contents lists available at SciVerse ScienceDirect Computer Vision and Image Understanding
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Research Article Image Superresolution Reconstruction via Granular Computing Clustering
"... which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and t ..."
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which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular computing (GrC) clustering is proposed by the hypersphere representation of granule and the fuzzy inclusion measure compounded by the operation between two granules. Thirdly, the granule set (GS) including hypersphere granules with different granularities is induced by GrC and used to form the relation between the LR image and the SR image by lasso. Experimental results showed that GrC achieved the least root mean square errors between the reconstructed SR image and the original image compared with bicubic interpolation, sparse representation, and NNLasso. 1.
LUNAR TERRAIN AND ALBEDO RECONSTRUCTION FROM APOLLO IMAGERY
"... Abstract. Generating accurate three dimensional planetary models and albedo maps is becoming increasingly more important as NASA plans more robotics missions to the Moon in the coming years. This paper describes a novel approach for separation of topography and albedo maps from orbital Lunar images. ..."
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Abstract. Generating accurate three dimensional planetary models and albedo maps is becoming increasingly more important as NASA plans more robotics missions to the Moon in the coming years. This paper describes a novel approach for separation of topography and albedo maps from orbital Lunar images. Our method uses an optimal Bayesian correlator to refine the stereo disparity map and generate a set of accurate digital elevation models (DEM). The albedo maps are obtained using a multi-image formation model that relies on the derived DEMs and the Lunar-Lambert reflectance model. The method is demonstrated on a set of high resolution scanned images from the Apollo era missions. 1.