Conference Proceeding

High-accuracy stereo depth maps using structured light

Middlebury Coll., VT, USA;
07/2003; 1:I-195- I-202 vol.1. DOI:10.1109/CVPR.2003.1211354 ISBN: 0-7695-1900-8 In proceeding of: Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, Volume: 1
Source: DBLP

ABSTRACT 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 pixel-accurate correspondence information using structured light. Unlike traditional range-sensing approaches, our method does not require the calibration of the light sources and yields registered disparity maps between all pairs of cameras and illumination projectors. We present new stereo data sets acquired with our method and demonstrate their suitability for stereo algorithm evaluation. Our results are available at http://www.middlebury.edu/stereo/.

0 0
 · 
1 Bookmark
 · 
116 Views
  • Source
    [show abstract] [hide abstract]
    ABSTRACT: In computer vision, two major active range imaging methods have been frequently employed for rapid and efficient shape recovery: (a) conventional active stereo vision and (b) conventional structured-light vision. This paper presents a comparative analysis and an integration of the two active approaches, namely, a structured-light stereo approach for the acquisition of dynamic shape. We first investigate the strengths and weaknesses of the two approaches in terms of accuracy, computational cost, field of view, depth of field, and color sensitivity. Based on this analysis, we propose a novel integrated method, the structured-light stereo, to recover dynamic shapes from a wider view with less occlusion by taking most of the benefits of the two approaches. The main idea is as follows. We first build a system composed of two cameras and a single projector (just a basic setup for conventional active stereo), and the projector projects a single “one-shot” color-stripe pattern. The next step is to estimate reliable correspondences between each camera and the projector via an accurate and efficient pattern decoding technique, and some remaining unresolved regions are explored by a stereo matching technique, which is less sensitive to object surface colors and defocus due to the projector's short depth of field, to estimate additional correspondences. We demonstrate the efficacy of the integrated method through experimental results.
    Optics and Lasers in Engineering 11/2013; 51(11):1255 - 1264. · 1.92 Impact Factor
  • Source
    Dataset: 1569582543
  • Source
    [show abstract] [hide abstract]
    ABSTRACT: Research interest in rapid structured-light imaging has grown increasingly for the modeling of moving objects, and a number of methods have been suggested for the range capture in a single video frame. The imaging area of a 3D object using a single projector is restricted since the structured light is projected only onto a limited area of the object surface. Employing additional projectors to broaden the imaging area is a challenging problem since simultaneous projection of multiple patterns results in their superposition in the light-intersected areas and the recognition of original patterns is by no means trivial. This paper presents a novel method of multi-projector color structured-light vision based on projector–camera triangulation. By analyzing the behavior of superposed-light colors in a chromaticity domain, we show that the original light colors cannot be properly extracted by the conventional direct estimation. We disambiguate multiple projectors by multiplexing the orientations of projector patterns so that the superposed patterns can be separated by explicit derivative computations. Experimental studies are carried out to demonstrate the validity of the presented method. The proposed method increases the efficiency of range acquisition compared to conventional active stereo using multiple projectors.
    Signal Processing Image Communication 10/2013; 28(9):1046 - 1058. · 1.29 Impact Factor

Full-text

View
1 Download
Available from

Daniel Scharstein