Abstract: A training system includes: a pose module configured to: receive an image captured using a camera; and determine a 6 degree of freedom (DoF) pose of the camera; and a training module configured to: input training images to the pose module from a training dataset; and train a segmentation module of the pose module by alternating between: updating a target distribution with parameters of the segmentation module fixed based on minimizing a first loss determined based on a label distribution determined based on prototype distributions determined by the pose module based on input of ones of the training images; updating the parameters of the segmentation module with the target distribution fixed based on minimizing a second loss determined based on a second loss that is different than the first loss; and updating the parameters of the segmentation module based on a ranking loss using a global representation.

Abstract: A system for detecting and describing keypoints in images is described. A camera is configured to capture an image including a plurality of pixels. A fully convolutional network is configured to jointly and concurrently: generate descriptors for each of the pixels, respectively; generate reliability scores for each of the pixels, respectively; and generate repeatability scores for each of the pixels, respectively. A scoring module is configured to generate scores for the pixels, respectively, based on the reliability scores and the repeatability scores of the pixels, respectively. A keypoint list module is configured to: select X of the pixels having the X highest scores, where X is an integer greater than 1; and generate a keypoint list including: locations of the selected X pixels; and the descriptors of the selected X pixels.

Abstract: A system for detecting and describing keypoints in images is described. A camera is configured to capture an image including a plurality of pixels. A fully convolutional network is configured to jointly and concurrently: generate descriptors for each of the pixels, respectively; generate reliability scores for each of the pixels, respectively; and generate repeatability scores for each of the pixels, respectively. A scoring module is configured to generate scores for the pixels, respectively, based on the reliability scores and the repeatability scores of the pixels, respectively. A keypoint list module is configured to: select X of the pixels having the X highest scores, where X is an integer greater than 1; and generate a keypoint list including: locations of the selected X pixels; and the descriptors of the selected X pixels.

Abstract: A method for training, using a plurality of training images with corresponding six degrees of freedom camera pose for a given environment and a plurality of reference images, each reference image depicting an object-of-interest in the given environment and having a corresponding two-dimensional to three-dimensional correspondence for the given environment, a neural network to provide visual localization by: for each training image, detecting and segmenting object-of-interest in the training image; generating a set of two-dimensional to two-dimensional matches between the detected and segmented objects-of-interest and corresponding reference images; generating a set of two-dimensional to three-dimensional matches from the generated set of two-dimensional to two-dimensional matches and the two-dimensional to three-dimensional correspondences corresponding to the reference images; and determining localization, for each training image, by solving a perspective-n-point problem using the generated set of two-dimens

Abstract: A method for training, using a plurality of training images with corresponding six degrees of freedom camera pose for a given environment and a plurality of reference images, each reference image depicting an object-of-interest in the given environment and having a corresponding two-dimensional to three-dimensional correspondence for the given environment, a neural network to provide visual localization by: for each training image, detecting and segmenting object-of-interest in the training image; generating a set of two-dimensional to two-dimensional matches between the detected and segmented objects-of-interest and corresponding reference images; generating a set of two-dimensional to three-dimensional matches from the generated set of two-dimensional to two-dimensional matches and the two-dimensional to three-dimensional correspondences corresponding to the reference images; and determining localization, for each training image, by solving a perspective-n-point problem using the generated set of two-dimens