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In this paper we have developed a new methodology to acquire highly precise and reliable ground truth disparity measurements accurately aligned with stereo image pairs

High-accuracy stereo depth maps using structured light

CVPR, (2003): 195-202

Cited: 1827|Views198
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Abstract

Recent progress in stereo algorithm performance is quickly outpacing the ability of existing stereo data sets to discriminate among the best-performing algorithms, motivating the need for more challenging scenes with accurate ground truth information. This paper describes a method for acquiring high-complexity stereo image pairs with pixe...More

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Introduction
  • The last few years have seen a resurgence of interest in the development of highly accurate stereo correspondence algorithms.
  • Part of this interest has been spurred by fundamental breakthroughs in matching strategies and optimization algorithms, and part of the interest is due to the existence of image databases that can be used to test and compare such algorithms.
  • Synthetic images have been suggested for testing stereo algorithm performance [12, 9], but they typically are either too easy to solve if noise, aliasing, etc. are not modeled, or too difficult, e.g., due to complete lack of texture in parts of the scene
Highlights
  • The last few years have seen a resurgence of interest in the development of highly accurate stereo correspondence algorithms
  • In this paper we have developed a new methodology to acquire highly precise and reliable ground truth disparity measurements accurately aligned with stereo image pairs
  • Such high-quality data is essential to evaluate the performance of stereo correspondence algorithms, which in turn spurs the development of even more accurate algorithms
  • Our new high-quality disparity maps and the original input images are available on our web site at http://www.middlebury.edu/stereo/
  • We plan to add these new data sets to those already in use to benchmark the performance of stereo correspondence algorithms [21]
  • Our novel approach is based on taking stereo image pairs illuminated with active lighting from one or more projectors
Results
  • Using the method described in the previous sections, the authors have acquired two different scenes, Cones and Teddy.
  • Figure 5 shows views L and R of each scene taken under ambient lighting.
  • L and R are views 3 and 7 out of a total of 9 images the authors have taken from -spaced viewpoints, which can be used for prediction-error evaluation [21].
  • The Cones scene was constructed such that most scene points visible from either view L and R can be illuminated with a single light source from above.
  • Due to the complex scene, several small areas are shadowed under both illuminations
Conclusion
  • In this paper the authors have developed a new methodology to acquire highly precise and reliable ground truth disparity measurements accurately aligned with stereo image pairs.
  • Such high-quality data is essential to evaluate the performance of stereo correspondence algorithms, which in turn spurs the development of even more accurate algorithms.
  • The encoded positions enable the recovery of camera-projector disparities, which can be used as an auxiliary source of information to increase the reliability of correspondences and to fill in missing data
Tables
  • Table1: Performance of SSD, dynamic programming, and graph cut stereo methods on our data sets. The table shows the percentage of pixels whose disparity error is greater than threshold t for t = 1, 2
Download tables as Excel
Funding
  • Describes a method for acquiring high-complexity stereo image pairs with pixel-accurate correspondence information using structured light
  • Presents new stereo data sets acquired with our method and demonstrate their suitability for stereo algorithm evaluation
  • Distinguishes between views – the images taken by the cameras – and illuminations – the structured light patterns projected onto the scene. models both processes using planar perspective projection and use coordinates for views and for illuminations
  • Has found that the only reliable way of thresholding pixels into on/off is to project both the code pattern and its inverse
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