Abstract: Techniques for efficiently identifying objects of interest in an environment and, thereafter, determining the location and/or orientation of those objects. As described below, a system may analyze images captured by a camera to identify objects that may be represented by the images. These objects may be identified in the images based on their size, color, and/or other physical attributes. After identifying these potential objects, the system may define a region around each object for further inspection. Thereafter, portions of a depth map of the environment corresponding to these regions may be analyzed to determine whether any of the objects identified from the images are “objects of interest”—or objects that the system has previously been instructed to track. These objects of interest may include portable projection surfaces, a user's hand, or any other physical object. The techniques identify these objects with reference to the respective depth signatures of these objects.

Abstract: Techniques for efficiently identifying objects of interest in an environment and, thereafter, determining the location and/or orientation of those objects. As described below, a system may analyze images captured by a camera to identify objects that may be represented by the images. These objects may be identified in the images based on their size, color, and/or other physical attributes. After identifying these potential objects, the system may define a region around each object for further inspection. Thereafter, portions of a depth map of the environment corresponding to these regions may be analyzed to determine whether any of the objects identified from the images are “objects of interest”—or objects that the system has previously been instructed to track. These objects of interest may include portable projection surfaces, a user's hand, or any other physical object. The techniques identify these objects with reference to the respective depth signatures of these objects.

Abstract: Techniques for efficiently identifying objects of interest in an environment and, thereafter, tracking the location and/or orientation of those objects. As described below, a system may analyze images captured by a camera to identify objects that may be represented by the images. These objects may be identified in the images based on their size, color, and/or other physical attributes. After identifying these potential objects, the system may define a region around each object for further inspection. Thereafter, portions of a depth map of the environment corresponding to these regions may be analyzed to determine whether any of the objects identified from the images are “objects of interest”—or objects that the system has previously been instructed to track. These objects of interest may include portable projection surfaces, a user's hand, or any other physical object. The techniques identify these objects with reference to the respective depth signatures of these objects.

Abstract: A calibration module may acquire calibration data for calibrating rotations of augmented reality functional node. The calibration module scans a target, records pose data for the augmented reality functional node, and captures images of the scanned target. From these acquired images, calibration may be determined. Once determined, the calibration may be used to determine a rotator control model.