Innovative techniques to generate a haptic stream are proposed. The proposed techniques allow haptic stream to be captured and along with audio/video stream. In so doing, a full experience—audio, video, haptics experience—may be experienced during playback.

A method and system for generating three-dimensional (3D) model augmented related objects from a scene are provided. The method includes creating one or more 3D objects and placing the 3D objects into the 3D scene. Embodiments herein disclose methods and systems for generating 3D augmented reality (AR) objects from a scene. The method may capture an object from the scene, perform a coarse semantic segmentation on the identified object, derive connected contour, generate intermediate contour from at least one outer and inner contours, and configure three-dimensional mesh and texture mapping to generate a three-dimensional model of the captured object.

A method and system for generating three-dimensional (3D) model augmented related objects from a scene are provided. The method includes creating one or more 3D objects and placing the 3D objects into the 3D scene. Embodiments herein disclose methods and systems for generating 3D augmented reality (AR) objects from a scene. The method may capture an object from the scene, perform a coarse semantic segmentation on the identified object, derive connected contour, generate intermediate contour from at least one outer and inner contours, and configure three-dimensional mesh and texture mapping to generate a three-dimensional model of the captured object.

A method for generating a three-dimensional image includes capturing a set of color images of an object, generating a first point cloud according to at least the set of color images, generating a second point cloud by performing a filtering operation to the first point cloud according to the set of color images, selectively performing a pairing operation using the second point cloud and a target point cloud to generate pose information, and combining the first point cloud and the target point cloud according to the pose information to update the target point cloud to generate the three-dimensional image of the object. The set of color images is related to color information of the object. The relativity of the second point cloud and the rigid surface is higher than the relativity of the second point cloud and the non-rigid surface.

A method for generating a three-dimensional image includes capturing a set of color images of an object, generating a first point cloud according to at least the set of color images, generating a second point cloud by performing a filtering operation to the first point cloud according to the set of color images, selectively performing a pairing operation using the second point cloud and a target point cloud to generate pose information, and combining the first point cloud and the target point cloud according to the pose information to update the target point cloud to generate the three-dimensional image of the object. The set of color images is related to color information of the object. The relativity of the second point cloud and the rigid surface is higher than the relativity of the second point cloud and the non-rigid surface.

The invention relates to method and system for classifying faces of a Boundary Representation (B-Rep) model using Artificial Intelligence (AI). The method includes extracting topological information corresponding to each of a plurality of data points of a B-Rep model of a product; determining a set of parameters based on the topological information corresponding to each of the plurality of data points; transforming the set of parameters corresponding to each of the plurality of data points of the B-Rep model into a tabular format to obtain a parametric data table; and assigning each of the plurality of faces of the B-Rep model a category from a plurality of categories based on the parametric data table using an AI model

The invention relates to method and system for classifying faces of a Boundary Representation (B-Rep) model using Artificial Intelligence (AI). The method includes extracting topological information corresponding to each of a plurality of data points of a B-Rep model of a product; determining a set of parameters based on the topological information corresponding to each of the plurality of data points; transforming the set of parameters corresponding to each of the plurality of data points of the B-Rep model into a tabular format to obtain a parametric data table; and assigning each of the plurality of faces of the B-Rep model a category from a plurality of categories based on the parametric data table using an AI model

Devices, systems, and methods of generating and providing a target topographic map for finishing a photomask blank are disclosed. A method includes receiving topographic data corresponding to an uncompleted photomask blank, receiving functional specifications for flatness of an acceptable photomask blank, and generating the target topographic map for first and/or second major surfaces of the blank, which provides instructions for removing material from the first and/or second major surfaces such that the first and second major surfaces achieve a flatness that passes each functional specification. The amount of material removed reflects a reduction in material necessary to pass the functional specifications. The method further includes transmitting the target topographic map to the finishing device to utilize a finishing technique to implement changes to the photomask blank according to the target topographic map by removing the material from the photomask blank to achieve a photomask blank that passes the functional specifications.

Provided is a robot, including: a plurality of sensors; a processor; a tangible, non-transitory, machine readable medium storing instructions that when executed by the processor effectuates operations including: capturing, with an image sensor, images of a workspace as the robot moves within the workspace; identifying, with the processor, at least one characteristic of at least one object captured in the images of the workspace; determining, with the processor, an object type of the at least one object based on characteristics of different types of objects stored in an object dictionary; and instructing, with the processor, the robot to execute at least one action based on the object type of the at least one object.

The present invention belongs to the technical field of petroleum exploitation engineering, and discloses a 3D modeling method for cementing hydrate sediment based on a CT image. Indoor remolding rock cores or in situ site rock cores without hydrate can be scanned by CT; a sediment matrix image stack and a pore image stack are obtained by gray threshold segmentation; then, a series of cementing hydrate image stacks with different saturations are constructed through image morphological processing of the sediment matrix image stack such as dilation, erosion and image subtraction operation; and a series of digital rock core image stacks of the cementing hydrate sediment with different saturations are formed through image subtraction operation and splicing operation to provide a relatively real 3D model for the numerical simulation work of the basic physical properties of a reservoir of natural gas hydrate.