Techniques are provided for one or more three-dimensional models representing one or more objects. For example, an input image including one or more objects can be obtained. From the input image, a location field can be generated for each object of the one or more objects. A location field descriptor can be determined for each object of the one or more objects, and a location field descriptor for an object of the one or more objects can be compared to a plurality of location field descriptors for a plurality of three-dimensional models. A three-dimensional model can be selected from the plurality of three-dimensional models for each object of the one or more objects. A three-dimensional model can be selected for the object based on comparing a location field descriptor for the object to the plurality of location field descriptors for the plurality of three-dimensional models.

Systems and techniques are provided for recalibrating cameras in a real space for tracking puts and takes of items by subjects. The method includes first processing one or more selected images selected from a plurality of sequences of images received from a plurality of cameras calibrated using a set of calibration images that were used to calibrate the cameras previously. The first processing includes a process step to extract a plurality of feature descriptors from the images. The first processing also includes a process step to match one or more feature descriptors as extracted from the selected images with feature descriptors extracted from the set of calibration images that were used to calibrate the cameras previously. The feature descriptors correspond to points located at displays or structures that remain substantially immobile.

A system comprising at least two three-dimensional (3D) cameras that are each configured to produce a digital image with a depth value for each pixel of the digital image; and a processor configured to: perform inter-camera calibration by: (i) estimating a pose of a subject, based, at least in part, on a skeleton representation of a subject captured each of by said at least two 3D cameras, wherein said skeleton representation identifies a plurality of skeletal joints of said subject, and (ii) enhancing the estimated pose based, at least in part, on a 3D point cloud of a scene containing the subject, as captured by each of said at least two 3D cameras, and perform data merging of digital images captured by said at least two 3D cameras, wherein the data merging is per each of said identifications.

A method for controlling a robotic device based on observed object locations is presented. The method includes observing objects in an environment. The method also includes generating a probability distribution for locations of the observed objects. The method further includes controlling the robotic device to perform an action in the environment based on the generated probability distribution.

A system and method for obtaining a 3D pose of an object using 2D images from multiple 2D cameras. The method includes positioning a first 2D camera so that it is directed towards the object along a first optical axis, obtaining 2D images of the object by the first 2D camera, and extracting feature points from the 2D images from the first 2D camera using a first feature extraction process. The method also includes positioning a second 2D camera so that it is directed towards the object along a second optical axis, obtaining 2D images of the object by the second 2D camera, and extracting feature points from the 2D images from the second 2D camera using a second feature extraction process. The method then estimates the 3D pose of the object using the extracted feature points from both of the first and second feature extraction process.

An augmented reality display system comprises a passable world model data comprises a set of map points corresponding to one or more objects of the real world. The augmented reality system also comprises a processor to communicate with one or more individual augmented reality display systems to pass a portion of the passable world model data to the one or more individual augmented reality display systems, wherein the piece of the passable world model data is passed based at least in part on respective locations corresponding to the one or more individual augmented reality display systems.

A method for controlling a robotic device includes observing a first object associated with an object type at one or more first locations in an environment over a period of time prior to a current time. The method also includes generating a probability distribution associated with the one or more first locations based on observing the first object over the period of time. The method further includes observing, at the current time, a second object associated with the object type at a second location in the environment. The method still further includes determining a probability of the second object being at the second location based on observing the second object at the second location. The probability is based on the probability distribution associated with the one or more first locations. The method also includes controlling the robotic device to perform an action based on the probability being less than a threshold.

Apparatuses, methods, and computer program products for safety compliance determinations are provided. An example method includes receiving three-dimensional (3D) image data indicative of a field of view of a 3D imager that includes a first user upon which to perform a compliance determination. The method further includes generating a fit parameter associated with a safety device of the first user within the field of view of the 3D imager based upon the 3D image data, the fit parameter indicative of an associated positioning of the safety device relative to the first user. The method also includes comparing the fit parameter with a compliance threshold associated with the safety device and generating an alert signal in an instance in which the fit parameter fails to satisfy the compliance threshold. In some instances, the method may supply the 3D image data to an artificial neural network to generate the fit parameter.

Methods, systems, and programs are presented for simultaneous recognition of objects within a detection space utilizing three-dimensional (3D) cameras configured for capturing 3D images of the detection space. One system includes the 3D cameras, calibrated based on a pattern in a surface of the detection space, a memory, and a processor. The processor combines data of the 3D images to obtain pixel data and removes, from the pixel data, background pixels of the detection space to obtain object pixel data associated with objects in the detection space. Further, the processor creates a geometric model of the object pixel data, the geometric model including surface information of the objects in the detection space, generates one or more cuts in the geometric model to separate objects and obtain respective object geometric models, and performs object recognition to identify each object in the detection space based on the respective object geometric models.

A probabilistic model is provided based on an output of a matching procedure that matches a particular object to representations of objects, where the probabilistic model relates a probability of an object being present to a number of matching features. The probabilistic model is used for detecting whether a particular object is present in received visual data.