Abstract: An apparatus for reconstructing a 3D object, includes a memory storing instructions, and at least one processor configured to execute the instructions to obtain, using a first neural network, mapping function weights of a mapping function of a second neural network, based on an image feature vector corresponding to a 2D image of the 3D object, set the mapping function of the second neural network, using the obtained mapping function weights, and based on sampled points of a canonical sampling domain, obtain, using the second neural network of which the mapping function is set, 3D point coordinates and geodesic lifting coordinates of each of the sampled points in the 3D object corresponding to the 2D image, wherein the 3D point coordinates are first three dimensions of an embedding vector of a respective one of the sampled points, and the geodesic lifting coordinates are remaining dimensions of the embedding vector.

Abstract: An apparatus for estimating a trajectory of a tool may include: a memory storing instructions; and a processor configured to execute the instructions to: receive a task to be performed by the tool on a target object; receive a grayscale image and a depth image of the target object; and estimate a tool trajectory for performing the task, from the grayscale image and the depth image, via a pixels-to-plans neural network that is trained based on a labeled tool trajectory that is generated from a point cloud model of the target object.

Abstract: An apparatus for representing a three-dimensional (3D) object, the apparatus includes a memory storing instructions, and a processor configured to execute the instructions to transmit a two-dimensional (2D) image to an external device, based on the 2D image being transmitted, receive, from the external device, mapping function parameters that are obtained using a first neural network, set a mapping function of a second neural network, based on the received mapping function parameters, and based on 3D samples, obtain the 3D object corresponding to the 2D image, using the second neural network of which the mapping function is set.

Abstract: A detection system includes a filter and a detector. The filter receives input packets from one or more dynamic vision sensors (DVSs) deployed in an area. The filter reduces the bandwidth of the input packets to produce compressed data packets while maintaining good end-to-end detection capability. The compressed data packets are transmitted to a detector in a low bandwidth transmission. The detector may collect compressed data packets from many filters. The detector determines if an object of interest is represented in the information of the input packets. If an object of interest is present, the detector activates an alarm, sends a notification to a person associated with the area and/or stores an indication in a database.

Abstract: A method of collision localization on a robotic device includes obtaining audio signals from a plurality of acoustic sensors spaced apart along the robotic device; identifying, based on a collision being detected, a strongest audio signal; identifying a primary onset time for an acoustic sensor producing the strongest audio signal, the primary onset time being a time at which waves propagating from the collision reach the acoustic sensor producing the strongest audio signal; generating a virtual onset time set, by shifting a calibration manifold, based on the identified primary onset time, the calibration manifold representing relative onset times from evenly spaced marker locations on the robotic device to the plurality of acoustic sensors; determining scores for the marker locations based a standard deviation of elements in the virtual onset time set; and estimating a location of the collision based on a highest score of the determined scores.

Abstract: A tool control system may include: a tactile sensor configured to, when a tool holds a target object and slides the target object downward across the tool, obtain tactile sensing data from the tool; one or more memories configured to store a target velocity and computer-readable instructions; and one or more processors configured execute the computer-readable instructions to: receive the tactile sensing data from the tactile sensor; estimate a velocity of the target object based on the tactile sensing data, by using one or more neural networks that are trained based on a training image of an sample object captured while the sample object is sliding down; and generate a control parameter of the tool based on the estimated velocity and the target velocity.

Abstract: An apparatus for representing a three-dimensional (3D) object, the apparatus includes a memory storing instructions, and a processor configured to execute the instructions to transmit a two-dimensional (2D) image to an external device, based on the 2D image being transmitted, receive, from the external device, mapping function parameters that are obtained using a first neural network, set a mapping function of a second neural network, based on the received mapping function parameters, and based on 3D samples, obtain the 3D object corresponding to the 2D image, using the second neural network of which the mapping function is set.

Abstract: A method may include obtaining a set of events, of a set of pixels of a dynamic vision sensor, associated with an object; determining a set of voltages of the set of pixels, based on the set of events; generating a set of images, based on the set of voltages of the set of pixels; inputting the set of images into a first neural network configured to output a visual motion estimation of the object; inputting the set of images into a second neural network configured to output a confidence score of the visual motion estimation output by the first neural network; obtaining the visual motion estimation of the object and the confidence score of the visual motion estimation of the object, based on inputting the set of images into the first neural network and the second neural network; and providing the visual motion estimation of the object and the confidence score.

Abstract: A method includes constructing a three-dimensional model of a front side of a row of trees based on a plurality of images of the front side of the row of trees and constructing a three-dimensional model of a back side of the row of trees based on a plurality of images of the back side of the row of trees. The three-dimensional model of the front side of the row of trees is merged with the three-dimensional model of the back side of the row of trees by linking a trunk in the three-dimensional model of the front side to a trunk in the three-dimensional model of the back side to form a merged three-dimensional model of the row of trees. The merged three-dimensional model of the row of trees is used to determine a physical attribute of the row of trees.

Abstract: An apparatus for representing a three-dimensional (3D) object, the apparatus includes a memory storing instructions, and a processor configured to execute the instructions to transmit a two-dimensional (2D) image to an external device, based on the 2D image being transmitted, receive, from the external device, mapping function parameters that are obtained using a first neural network, set a mapping function of a second neural network, based on the received mapping function parameters, and based on 3D samples, obtain the 3D object corresponding to the 2D image, using the second neural network of which the mapping function is set.

Abstract: An apparatus for representing a three-dimensional (3D) object, the apparatus includes a memory storing instructions, and a processor configured to execute the instructions to transmit a two-dimensional (2D) image to an external device, based on the 2D image being transmitted, receive, from the external device, mapping function parameters that are obtained using a first neural network, set a mapping function of a second neural network, based on the received mapping function parameters, and based on 3D samples, obtain the 3D object corresponding to the 2D image, using the second neural network of which the mapping function is set.

Abstract: An apparatus for estimating a trajectory of a tool may include: a memory storing instructions; and a processor configured to execute the instructions to: receive a task to be performed by the tool on a target object; receive a grayscale image and a depth image of the target object; and estimate a tool trajectory for performing the task, from the grayscale image and the depth image, via a pixels-to-plans neural network that is trained based on a labeled tool trajectory that is generated from a point cloud model of the target object.

Abstract: A method includes constructing a three-dimensional model of a front side of a row of trees based on a plurality of images of the front side of the row of trees and constructing a three-dimensional model of a back side of the row of trees based on a plurality of images of the back side of the row of trees. The three-dimensional model of the front side of the row of trees is merged with the three-dimensional model of the back side of the row of trees by linking a trunk in the three-dimensional model of the front side to a trunk in the three-dimensional model of the back side to form a merged three-dimensional model of the row of trees. The merged three-dimensional model of the row of trees is used to determine a physical attribute of the row of trees.

Abstract: A method for generating improved image mosaics includes using a set of overlapping images of a landscape to determine three-dimensional locations for a plurality of features of the landscape. Based on the three-dimensional locations of the plurality of features, a three-dimensional plane of interest is determined. The overlapping images are warped onto the plane of interest to form a mosaic of images.

Abstract: A method for generating improved image mosaics includes using a set of overlapping images of a landscape to determine three-dimensional locations for a plurality of features of the landscape. Based on the three-dimensional locations of the plurality of features, a three-dimensional plane of interest is determined. The overlapping images are warped onto the plane of interest to form a mosaic of images.

Abstract: A method of machine vision includes identifying contours of fruits in a first image and a second image and performing two-way matching of contours to identify pairs of matched contours, each pair comprising a respective first contour in the first image that matches a respective second contour in the second image. For each pair of matched contours, a respective affine transformation that transforms points in the respective second contour to points in the respective first contour is identified. The second image is mapped to the first image using the affine transformations to form a composite image and the number of fruits in the composite image is counted.

Abstract: A method of machine vision includes identifying contours of fruits in a first image and a second image and performing two-way matching of contours to identify pairs of matched contours, each pair comprising a respective first contour in the first image that matches a respective second contour in the second image. For each pair of matched contours, a respective affine transformation that transforms points in the respective second contour to points in the respective first contour is identified. The second image is mapped to the first image using the affine transformations to form a composite image and the number of fruits in the composite image is counted.

Abstract: A system includes an unmanned aerial vehicle and an unmanned surface vehicle. The unmanned aerial vehicle has a memory storing a plurality of collection points and at least one sensor for collecting sensor data from each of the collection points. The unmanned surface vehicle is capable of moving to a plurality of locations. The unmanned aerial vehicle travels through the air between at least two collection points stored in the memory and the unmanned aerial vehicle is carried between at least two collection points stored in the memory by the unmanned surface vehicle.

Abstract: A system includes an unmanned aerial vehicle and an unmanned surface vehicle. The unmanned aerial vehicle has a memory storing a plurality of collection points and at least one sensor for collecting sensor data from each of the collection points. The unmanned surface vehicle is capable of moving to a plurality of locations. The unmanned aerial vehicle travels through the air between at least two collection points stored in the memory and the unmanned aerial vehicle is carried between at least two collection points stored in the memory by the unmanned surface vehicle.