Abstract: A method calibrates one or more cameras, wherein the one or more cameras acquire images of a scene for different viewpoints, and wherein the images are non-overlapping, by first constructing a 3D model of the scene using a calibration camera that is independent of the one or more cameras, and a simultaneous localization and mapping (SLAM) procedure. 2D-to-3D correspondences between each of the images and the 3D model are determined. Then, calibration parameters of each of the one or more cameras are estimated using a 2D-to-3D registration procedure.

Abstract: A method and system detects and localizes multiple instances of an object by first acquiring a frame of a three-dimensional (3D) scene with a sensor, and extracting features from the frame. The features are matched according to appearance similarity and triplets are formed among matching features. Based on 3D locations of the corresponding points in the matching triplets, a geometric transformation is computed. Matching triplets are clustered according to the computed geometric transformations. Since the set of features coining from two different object instances should have a single geometric transform, the output of clustering provides the features and poses of each object instance in the image.

Abstract: An imaging system for localization and mapping of a scene including static and dynamic objects. A sensor acquires a sequence of frames in motion or stationary. A memory to store a static map of static objects and an object map of each dynamic object in the scene. The static map includes a set of landmarks, and the object map includes a set of landmarks and a set of segments. A localizer registers keypoints of the frame with landmarks in the static map using frame-based registration and to register some segments in the frame with segments in the object map using a segment-based registration. A mapper to update each object map with keypoints forming each segment and keypoints registered with the corresponding object map according to the segment-based registration, and to update the static map with the remaining keypoints in the frame using the keypoints registered with the static map.

Abstract: A system for active-training a neural network includes an input interface to receive a set of images, a memory to store an active sampler, a feature generator and a subset of the images selected from the set of the images, the subset of the images having similarity values based on a predetermined feature domain information, at least one processor to train the feature generator by use of the active sampler. The active sampler is configured to perform first-selecting, from the subset of the images, a pair of images as matching pair images according to a threshold of similarity values, second-selecting a pair of images from another subset of the images, feeding the matching pair images and the unmatched pair images to the feature generator, updating weighting parameters and increasing the threshold according to preset values an output interface to render the weighting parameters of the feature generator.

Abstract: A system for active-training a neural network includes an input interface to receive a set of images, a memory to store an active sampler, a feature generator and a subset of the images selected from the set of the images, the subset of the images having similarity values based on a predetermined feature domain information, at least one processor to train the feature generator by use of the active sampler. The active sampler is configured to perform first-selecting, from the subset of the images, a pair of images as matching pair images according to a threshold of similarity values, second-selecting a pair of images from another subset of the images, feeding the matching pair images and the unmatched pair images to the feature generator, updating weighting parameters and increasing the threshold according to preset values an output interface to render the weighting parameters of the feature generator.

Abstract: A system for reconstructing a three-dimensional (3D) model of a scene including a point cloud having points identified by 3D coordinates includes at least one sensor to acquire a set of images of the scene from different poses defining viewpoints of the images and a memory to store the set of images and the 3D model of the scene. The system also includes a processor operatively connected to the memory and coupled with stored instructions to transform the images from the set of images to produce a set of virtual images of the scene viewed from virtual viewpoints; compare at least some features from the images and the virtual images to determine the viewpoint of each image in the set of images; and update 3D coordinates of at least one point in the model of the scene to match coordinates of intersections of ray back-projections from pixels of at least two images corresponding to the point according to the viewpoints of the two images.

Abstract: An image processing system includes an input interface to receive a set of pairs of images to train an image generator, each pair includes a low-resolution image of a scene and a high-resolution image of the scene, a processor to train the image generator by solving an optimization problem to produce parameters of the image generator reducing distances between image gradients of the high-resolution images and the corresponding low-resolution images upsampled by the image generator, and an output interface to render the parameters of the image generator.

Abstract: An image processing system includes an input interface to receive a set of pairs of images to train an image generator, each pair includes a low-resolution image of a scene and a high-resolution image of the scene, a processor to train the image generator by solving an optimization problem to produce parameters of the image generator reducing distances between image gradients of the high-resolution images and the corresponding low-resolution images upsampled by the image generator, and an output interface to render the parameters of the image generator.

Abstract: An imaging system for localization and mapping of a scene including static and dynamic objects. A sensor acquires a sequence of frames in motion or stationary. A memory to store a static map of static objects and an object map of each dynamic object in the scene. The static map includes a set of landmarks, and the object map includes a set of landmarks and a set of segments. A localizer registers keypoints of the frame with landmarks in the static map using frame-based registration and to register some segments in the frame with segments in the object map using a segment-based registration. A mapper to update each object map with keypoints forming each segment and keypoints registered with the corresponding object map according to the segment-based registration, and to update the static map with the remaining keypoints in the frame using the keypoints registered with the static map.

Abstract: A method and system detects and localizes multiple instances of an object by first acquiring a frame of a three-dimensional (3D) scene with a sensor, and extracting features from the frame. The features are matched according to appearance similarity and triplets are formed among matching features. Based on 3D locations of the corresponding points in the matching triplets, a geometric transformation is computed. Matching triplets are clustered according to the computed geometric transformations. Since the set of features coining from two different object instances should have a single geometric transform, the output of clustering provides the features and poses of each object instance in the image.

Abstract: A system for reconstructing a three-dimensional (3D) model of a scene including a point cloud having points identified by 3D coordinates includes at least one sensor to acquire a set of images of the scene from different poses defining viewpoints of the images and a memory to store the set of images and the 3D model of the scene. The system also includes a processor operatively connected to the memory and coupled with stored instructions to transform the images from the set of images to produce a set of virtual images of the scene viewed from virtual viewpoints; compare at least some features from the images and the virtual images to determine the viewpoint of each image in the set of images; and update 3D coordinates of at least one point in the model of the scene to match coordinates of intersections of ray back-projections from pixels of at least two images corresponding to the point according to the viewpoints of the two images.

Abstract: A method and system for registering data by first acquiring the data from a scene by a sensor at different viewpoints, and extracting, from the data, three-dimensional (3D) points and 3D lines, and descriptors associated with the 3D points and the 3D lines. A first set of primitives represented in a first coordinate system of the sensor is selected, wherein the first set of primitives includes at least three 3D points. A second set of primitives represented in a second coordinate system is selected, wherein the second set of primitives includes any combination of 3D points and 3D lines to obtain at least three primitives. Then, using the first set of primitives and the second set of primitives, the 3D points are registered with each other, and the 3D points with the 3D lines to obtain registered primitives, wherein the registered primitives are used in a simultaneous localization and mapping (SLAM) system.

Abstract: A method and system detects and localizes an object by first acquiring a frame of a three-dimensional (3D) scene with a sensor, and extracting features from the frame. The frame are segmented into segments, wherein each segment includes one or more features, and for each segment, searching an object map for a similar segment, and only if there is a similar segment in the object map, registering the segment in the frame with the similar segment to obtain a predicted pose of the object. The predicted poses are combined to obtain the pose of the object, which can be outputted.

Abstract: A method and system for registering data by first acquiring the data from a scene by a sensor at different viewpoints, and extracting, from the data, three-dimensional (3D) points and 3D lines, and descriptors associated with the 3D points and the 3D lines. A first set of primitives represented in a first coordinate system of the sensor is selected, wherein the first set of primitives includes at least three 3D points. A second set of primitives represented in a second coordinate system is selected, wherein the second set of primitives includes any combination of 3D points and 3D lines to obtain at least three primitives. Then, using the first set of primitives and the second set of primitives, the 3D points are registered with each other, and the 3D points with the 3D lines to obtain registered primitives, wherein the registered primitives are used in a simultaneous localization and mapping (SLAM) system.

Abstract: A method calibrates one or more cameras, wherein the one or more cameras acquire images of a scene for different viewpoints, and wherein the images are non-overlapping, by first constructing a 3D model of the scene using a calibration camera that is independent of the one or more cameras, and a. simultaneous localization and mapping (SLAM) procedure. 2D-to-3D correspondences between each of the images and the 3D model are determined. Then, calibration parameters of each of the one or more cameras are estimated using a 2D-to-3D registration procedure.