An augmented reality (AR) device can be configured to generate a virtual representation of a user's physical environment. The AR device can capture images of the user's physical environment to generate a mesh map. The AR device can project graphics at designated locations on a virtual bounding box to guide the user to capture images of the user's physical environment. The AR device can provide visual, audible, or haptic guidance to direct the user of the AR device to look toward waypoints to generate the mesh map of the user's environment.

Method and apparatus for overlaying themed imagery onto real-world objects in a head-mounted display device (HMDD). A computing device receives, from an HMDD, depth data that identifies distances from the HMDD to surfaces of a plurality of objects in a user space. The computing device detects at least one object in the user space based at least in part on the depth data. The computing device determines a classification of video content being presented on a display system of the HMDD. The computing device selects, based on the classification, a particular image theme from a plurality of different image themes, the image theme comprising one or more image textures. The computing device sends, to the HMDD, at least one image texture for overlaying the at least one object during presentation of the at least one object on the display system of the HMDD in conjunction with the video content.

Described herein are systems and methods of converting media dimensions. A device may identify a set of frames from a video in a first orientation as belonging to a scene. The device may receive a selected coordinate on a frame of the set of frames for the scene. The device may identify a first region within the frame including a first feature corresponding to the selected coordinate and a second region within the frame including a second feature. The device may generate a first score for the first feature and a second score for the second feature. The first score may be greater than the second score based on the first feature corresponding to the selected coordinate. The device may crop the frame to include the first region and the second region within a predetermined display area comprising a subset of regions of the frame in a second orientation.

In an example, a digital image comprising a representation of multiple physical objects is received at a client computer. The digital image is copied into a temporary canvas. The digital image is then analyzed to identify a plurality of potential object areas, each of the potential object areas having pixels with colors similar to the other pixels within the potential object area. A minimum bounding region for each of the identified potential object areas is identified, the minimum bounding region being a smallest region of a particular shape that bounds the corresponding potential object area. The pixels within a selected minimum bounding region are cropped from the digital image. The pixels within the selected minimum bounding region are then sent to an object recognition service on a server to identify an object represented by the pixels within the selected minimum bounding region.

An occlusion metric is computed for a target object in a 3D multi-object simulation. The target object is represented in 3D space by a collision surface and a 3D bounding box. In a reference surface defined in 3D space, a bounding box projection is determined for the target object with respect to an ego location. The bounding box projection is used to determine a set of reference points in 3D space. For each reference point of the set of reference points, a corresponding ray is cast based on the ego location, and it is determined whether the ray is an object ray that intersects the collision surface of the target object. For each such object ray, it is determined whether the object ray is occluded. The occlusion metric conveys an extent to which the object rays are occluded.

Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a computing system can receive object data that includes portions of sensor data. The computing system can determine, in a first stage of a multiple stage classification using hardware components, one or more first stage characteristics of the portions of sensor data based on a first machine-learned model. In a second stage of the multiple stage classification, the computing system can determine second stage characteristics of the portions of sensor data based on a second machine-learned model. The computing system can generate an object output based on the first stage characteristics and the second stage characteristics. The object output can include indications associated with detection of objects in the portions of sensor data.

According to an embodiment, a computer-implemented method for generating an edge curve to facilitate manufacture of at least a portion of a dental device is disclosed. The method includes identifying at least one tooth reference point for each of at least two teeth on a dental model; identifying at least one offset point corresponding to each of the at least one tooth reference points such that the at least one offset point is on a gingival surface of the dental model and located outside an interproximal area; and generating the edge curve by connecting the offset points such that the edge curve is outside the interproximal area and on the gingival surface.

Among other things, techniques are described for cross-modality active learning for object detection. In an example, a first set of predicted bounding boxes and a second set of predicted bounding boxes is generated. The first set of predicted bounding boxes and the second set of predicted bounding boxes are projected into a same representation. The projections are filtered, wherein predicted bounding boxes satisfying a maximum confidence score are selected for inconsistency calculations. Inconsistencies are calculated across the projected bounding boxes based on filtering the projections. An informative scene is extracted based on the calculated inconsistencies. A first object detection neural network or a second object detection neural network is trained using the informative scenes.

The system, devices and methods herein enable autonomous and tele-operation of tele-operated robots for maintenance of a property around known and unknown obstacles. A method may include using an unmanned aerial vehicle for obtaining additional data relating to the property and obstacles within the property and plan a path around the obstacles using data from sensors on-board the tele-operated robot and the aerial image. A method may also provide optimization of total time needed for performing the property maintenance and the labor costs in situations where manual intervention is needed for navigating the tele-operated robot around obstacles on the property or for removing obstacles on the property.

Various implementations disclosed herein include devices, systems, and methods that generate floorplans and measurements using a three-dimensional (3D) representation of a physical environment generated based on sensor data.