Abstract: A system and/or method for imaging system calibration, including: determining a dense correspondence map matching features in a first image to features in a second image, and determining updated calibration parameters associated with the image acquisition system based on the matching features.

Abstract: A system and/or method for imaging system calibration, including: determining a dense correspondence map matching features in a first image to features in a second image, and determining updated calibration parameters associated with the image acquisition system based on the matching features.

Abstract: A system and method for determining egomotion can include determining correspondence maps between pairs of images of an odometry set; identifying odometry features shared between the images of the odometry set; and determining the egomotion based on the odometry features.

Abstract: A system and/or method for imaging system calibration, including: determining a dense correspondence map matching features in a first image to features in a second image, and determining updated calibration parameters associated with the image acquisition system based on the matching features.

Abstract: A system and method for determining a correspondence map between a first and second image by determining a set of correspondence vectors for each pixel in the first image and selecting a correspondence vector from the set of correspondence vectors based on a cost value.

Abstract: A system and/or method for imaging system calibration, including: determining a dense correspondence map matching features in a first image to features in a second image, and determining updated calibration parameters associated with the image acquisition system based on the matching features.

Abstract: A depth sensing system receives an image pair showing objects in a scene. The system generates binary hashes for pixels in the image pair by performing a random walk. The system matches pixels in the first image to pixels in the second image that depict the same point in the scene by generating cost values representing differences between the binary hashes for pairs of pixels in the images. The system generates a disparity map containing disparity vectors representing coordinate differences between matched pixels in the first and second images. The system generates and outputs a depth map based on the disparity map. The depth map represents the distances between an image acquisition system that acquired the image pair and the objects in the scene.

Abstract: A system and method for determining egomotion can include determining correspondence maps between pairs of images of an odometry set; identifying odometry features shared between the images of the odometry set; and determining the egomotion based on the odometry features.

Abstract: A system and method for determining a correspondence map between a first and second image by determining a set of correspondence vectors for each pixel in the first image and selecting a correspondence vector from the set of correspondence vectors based on a cost value.

Abstract: A depth sensing system receives an image pair showing objects in a scene. The system generates binary hashes for pixels in the image pair by performing a random walk. The system matches pixels in the first image to pixels in the second image that depict the same point in the scene by generating cost values representing differences between the binary hashes for pairs of pixels in the images. The system generates a disparity map containing disparity vectors representing coordinate differences between matched pixels in the first and second images. The system generates and outputs a depth map based on the disparity map. The depth map represents the distances between an image acquisition system that acquired the image pair and the objects in the scene.