Dalian University of Technology

Peng Cheng Laboratory

Shengquan Li

Zhongxuan Luo

Xin Fan

Wei Zhong

Risheng Liu

Hong Zhang

Haojie Li

Zhihui Wang

Deyun Lv

Weiqiang Kong

The present invention discloses a disparity estimation optimization method based on upsampling and exact rematching, which conducts exact rematching within a small range in an optimized network, improves previous upsampling methods such as neighbor interpolation and bilinear interpolation for disparity maps or cost maps, and works out a propagation-based upsampling method by the way of network so that accurate disparity values can be better restored from disparity maps in the upsampling process.

Disparity estimation optimization method based on upsampling and exact rematching

A disparity image fusion method for multiband stereo cameras belongs to the field of image processing and computer vision. The method obtains pixel disparity confidence information by using the intermediate output of binocular disparity estimation. The confidence information can be used to judge the disparity credibility of the position and assist disparity fusion. The confidence acquisition process makes full use of the intermediate output of calculation, and can be conveniently embedded into the traditional disparity estimation process, with high calculation efficiency and simple and easy operation. In the disparity image fusion method for multiband stereo cameras proposed by the method, the disparity diagrams participating in the fusion are obtained according to the binocular images of the corresponding bands, which makes full use of the information of each band and simultaneously avoiding introducing uncertainty and errors.

Disparity image fusion method for multiband stereo cameras

The present invention belongs to the field of image processing and computer vision, and discloses an acceleration method of depth estimation for multiband stereo cameras. In the process of depth estimation, during binocular stereo matching in each band, through compression of matched images, on one hand, disparity equipotential errors caused by binocular image correction can be offset to make the matching more accurate, and on the other hand, calculation overhead is reduced. In addition, before cost aggregation, cost diagrams are transversely compressed and sparsely matched, thereby reducing the calculation overhead again. Disparity diagrams obtained under different modes are fused to obtain all-weather, more complete and more accurate depth information.

Acceleration method of depth estimation for multiband stereo cameras

The present invention discloses a method for adaptively detecting chessboard sub-pixel level corner points. Adaptive detection of chessboard sub-pixel level corner points is completed by marking position of an initial unit grid on a chessboard, using a homography matrix H calculated by pixel coordinates of four corner points of the initial unit grid in a pixel coordinate system and world coordinates in a world coordinate system to expand outwards, adaptively adjusting size of an iteration window in the process of expanding outwards, and finally spreading to the whole chessboard region.

Method for adaptively detecting chessboard sub-pixel level corner points

The present invention discloses a disparity estimation method for weakly supervised trusted cost propagation, which utilizes a deep learning method to optimize the initial cost obtained by the traditional method. By combining and making full use of respective advantages, the problems of false matching and difficult matching of untextured regions in the traditional method are solved, and the method for weakly supervised trusted cost propagation avoids the problem of data label dependency of the deep learning method.

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Liaoning, China

Shengquan Li