A vehicle includes an image capturing device configured to capture an image in a vehicle traveling direction, a display device, and a controller configured to carry out an analysis of the image captured by the image capturing device and display an indicator based on the analysis on the display device. The controller is configured to display a first object that approaches a host vehicle on the display device in a mode different from a mode for a second object that does not approach the host vehicle within the image captured by the image capturing device.

A system and a method predict a collision free posture of a kinematic system. The method includes: receiving a 3D virtual environment, receiving a 3D representation of the kinematic system and a set of 3D postures defined for the 3D virtual kinematic system, receiving a target task to be performed by the kinematic system with respect to the surrounding environment, and receiving a prescribed location within the 3D virtual environment. The prescribed location defines a position at which the 3D virtual kinematic system has to be placed within the 3D virtual environment. A collision free detection function (CFD) is applied to a set of input data containing the 3D virtual environment, the target task, the prescribed location and the set of postures. The CFD function outputs a set of collision free postures enabling the kinematic system to perform the target task when located at the prescribed location.

This disclosure relates generally to method and system for draping a 3D garment on a 3D human body. Dressing digital humans in 3D have gained much attention due to its use in online shopping and draping 3D garments over the 3D human body has immense applications in virtual try-on, animations, and accurate fitment of the 3D garment is the utmost importance. The proposed disclosure is a single unified garment deformation model that learns the shared space of variations for a body shape, a body pose, and a styling garment. The method receives a plurality of human body inputs to construct a 3D skinned garments for the subject. The deep draper network trained using a plurality of losses provides efficient deep neural network based method that predicts fast and accurate 3D garment images. The method couples the geometric and multi-view perceptual constraints that efficiently learn the garment deformation's high-frequency geometry.

Methods and ray tracing units are provided for performing intersection testing for use in rendering an image of a 3-D scene. A hierarchical acceleration structure may be traversed by traversing one or more upper levels of nodes of the hierarchical acceleration structure according to a first traversal technique, the first traversal technique being a depth-first traversal technique; and traversing one or more lower levels of nodes of the hierarchical acceleration structure according to a second traversal technique, the second traversal technique not being a depth-first traversal technique. Results of traversing the hierarchical acceleration structure are used for rendering the image of the 3-D scene. The upper levels of the acceleration structure may be defined according to a spatial subdivision structure, whereas the lower levels of the acceleration structure may be defined according to a bounding volume structure.

A computer-implemented method for procedurally simulating braided strands of fibers may include, under the control of one or more computer systems configured with executable instructions, obtaining a set of parameters of the braided strands of the fibers, the set of parameters indicating a braid spine, generating, based at least in part on the set of parameters, a set of interlacing strand spines that follow the braid spine within a tolerance according to the set of parameters, and computing a set of first geometric structures corresponding to the set of interlacing strand spines.

A method for processing an image may include: acquiring a target image; segmenting a target object in the target image, and determining a mask image according to a segmentation result; rendering the target object according to the target image and the mask image and determining a rendering result; and performing AR displaying according to the rendering result. A device and storage medium may implement the method.

A computer simulation object such as a chair is described by voice or photo input to render a 2D image. Machine learning may be used to convert voice input to the 2D image. The 2D image is converted to a 3D object and the 3D object or portions thereof are used in the computer simulation, such as a computer game, as the object such as a chair. A physics engine can be used to modify the 3D objects.

A system and method for modeling an enclosed space involves measuring ranges and angles between a static vantage point and points on surfaces enclosing the space using a single point time of flight distance measuring device. A computer coupled to the distance measuring device generate virtual surfaces and calculates where the virtual surfaces intersect to generate a geometry for a 3D model representing the surfaces enclosing the space.

Techniques are disclosed for improving the throughput of ray intersection or visibility queries performed by a ray tracing hardware accelerator. Throughput is improved, for example, by releasing allocated resources before ray visibility query results are reported by the hardware accelerator. The allocated resources are released when the ray visibility query results can be stored in a compressed format outside of the allocated resources. When reporting the ray visibility query results, the results are reconstructed based on the results stored in the compressed format. The compressed format storage can be used for ray visibility queries that return no intersections or terminate on any hit ray visibility query. One or more individual components of allocated resources can also be independently deallocated based on the type of data to be returned and/or results of the ray visibility query.

Provided are methods, systems, and computer-program products for recovering from intersections during a simulation of an animated scene when a collision detection operation is active. For example, the collision detection operation can be selectively activated and deactivated during the simulation of one or more objects for a time step based on an intersection analysis, which can identify intersections of the one or more objects for the time step. Once the collision detection operation is deactivated, a collision response can apply one or more forces to intersecting portions of the one or more objects to eliminate the intersections of the one or more objects. For example, a portion of a cloth that is in a state of intersection can be configured such that the collision detection operation is not performed on the portion, thereby allowing the cloth to be removed from inside of another object by a collision response algorithm.