Abstract: An example system for generating a three dimensional (3D) model includes a receiver to receive a single two dimensional (2D) image of an object to be modeled. The system includes a segment extractor to extract a binary segment, a textured segment, and a segment characterization based on the single 2D image. The system further includes a skeleton cue extractor to generate a medial-axis transform (MAT) approximation based on the binary segment and the segment characterization and extract a skeleton cue and a regression cue from the MAT approximation. The system also includes a contour generator to generate a contour based on the binary segment and the regression cue. The system can also further include a 3D model generator to generate a 3D model based on the contour and the skeleton cue.

Abstract: Data is received describing a local model of a first device generated by the first device based on sensor readings at the first device and a global model is updated that is hosted remote from the first device based on the local model and modeling devices in a plurality of different asset taxonomies. A particular operating state affecting one or more of a set of devices deployed in a particular machine-to-machine network is detected and the particular machine-to-machine network is automatically reconfigured based on the global model.

Abstract: An autonomous object modeler includes a modeling table, a controllable arm, a depth camera attached to the controllable arm, and a controller to control operation of the modeling table, the controllable arm, and the depth camera. The modeling table may be movable and includes a mass sensor to produce mass sensor data indicative of a mass of an object positioned on the modeling table. The controllable arm includes a force-torque sensor to produce force-torque sensor data indicative of an inertia of the object while the object is moved by the controllable arm. The controller is configured to control operation of the controllable arm to reposition the object on the modeling table to generate three-dimensional models of the object. The three-dimensional models include the mass data and the inertia data.

Abstract: Techniques for high-fidelity three-dimensional (3D) reconstruction of a dynamic scene as a set of voxels are provided. One technique includes: receiving, by a processor, image data from each of two or more spatially-separated sensors observing the scene from a corresponding two or more vantage points; generating, by the processor, the set of voxels from the image data on a frame-by-frame basis; reconstructing, by the processor, surfaces from the set of voxels to generate low-fidelity mesh data; identifying, by the processor, performers in the scene from the image data; obtaining, by the processor, high-fidelity mesh data corresponding to the identified performers; and merging, by the processor, the low-fidelity mesh data with the high-fidelity mesh data to generate high-fidelity 3D output. The identifying of the performers includes: segmenting, by the processor, the image data into objects; and classifying, by the processor, those of the objects representing the performers.

Abstract: An example system for generating a three dimensional (3D) model includes a receiver to receive a single two dimensional (2D) image of an object to be modeled. The system includes a segment extractor to extract a binary segment, a textured segment, and a segment characterization based on the single 2D image. The system further includes a skeleton cue extractor to generate a medial-axis transform (MAT) approximation based on the binary segment and the segment characterization and extract a skeleton cue and a regression cue from the MAT approximation. The system also includes a contour generator to generate a contour based on the binary segment and the regression cue. The system can also further include a 3D model generator to generate a 3D model based on the contour and the skeleton cue.

Abstract: Systems, apparatuses, and methods for trusted vehicle messaging may include receiving a communication from one or more of an internal vehicle component or an external communication system and composing a trusted message to be displayed in response to the received communication. Content may be managed to be displayed on one or more displays supported by a body of a vehicle.

Abstract: A mechanism is described for facilitating generation of voxel representations and assignment of trust metrics according to one embodiment. A method of embodiments, as described herein, includes detecting, by a computing device, a plurality of data sources hosting image capturing devices capable of capturing images of objects in a space, where the images are capable of being received by the computing device. The method may further include tagging trust metrics to the images based on credibility of the plurality of data sources, where a trust metrics to indicate veracity of a corresponding image. The method may further include aggregating the images into an aggregated image representation, and generating a voxel representation of the aggregated image representation such that the images are presented as voxel images, where veracities of the voxel images are secured based on tagging of the trust metrics to their corresponding images.

Abstract: According to various embodiments, devices, methods, and computer-readable media for reconstructing a 3D scene are described. A server device, sensor devices, and client devices may interoperate to reconstruct a 3D scene sensed by the sensor devices. The server device may generate one or more models for objects in the scene, including the identification of dynamic and/or static objects. The sensor devices may, provide model data updates based on these generated models, such that only delta changes in the scene may be provided, in addition to raw sensor data. Models may utilize semantic knowledge, such as knowledge of the venue or identity of one or more persons in the scene, to further facilitate model generation and updating. Other embodiments may be described and/or claimed.