Abstract: The present subject matter refers a virtual object positioning method in an augmented reality (AR) or virtual reality (VR) environment. The method comprises receiving a virtual object for positioning relative to one or more objects in an AR or VR scene. During the positioning, a relative position of the virtual object is detected relative to the one or more objects in the AR or VR scene and a position due to the relative movement is detected. An occurrence of change in a physical characteristic of the virtual object is predicted based on the virtual object crossing a boundary with respect to the one or more objects. It is determined that the virtual object is likely to cross the boundary during the positioning. Upon the determination, a prior alert is generated.

Abstract: A method for selective magnification in a 3D rendering system includes: displaying, by an electronic device, a first view including at least one real world object and at least one virtual object; displaying, by the electronic device, a second view comprising a region of interest (ROI) in the first view, while displaying the first view; receiving, by the electronic device, a user interaction on the second view; and generating, by the electronic device, interaction on the first view corresponding to the user interaction received on the second view.

Abstract: An apparatus and method are provided for compressing a three-dimensional (3D) object image represented by point cloud data. The method includes positioning the 3D object image into a plurality of equi-sized cubes for compression; determining 3D local coordinates in each of the plurality of equi-sized cubes and a cube index for each point of the 3D object image positioned in the plurality of equi-sized cubes; generating two-dimensional (2D) image data based on the 3D local coordinates and the cube indexes; and storing the 2D image data in a memory. The 2D image data includes at least one of 2D geometry data, 2D meta data, or 2D color data.

Abstract: An apparatus and method are provided for compressing a three-dimensional (3D) object image represented by point cloud data. The method includes positioning the 3D object image into a plurality of equi-sized cubes for compression; determining 3D local coordinates in each of the plurality of equi-sized cubes and a cube index for each point of the 3D object image positioned in the plurality of equi-sized cubes; generating two-dimensional (2D) image data based on the 3D local coordinates and the cube indexes; and storing the 2D image data in a memory. The 2D image data includes at least one of 2D geometry data, 2D meta data, or 2D color data.

Abstract: A parallelization method includes: obtaining profiling information for each job step of a job by performing profiling of the job to be executed on an electronic device; determining at least one job step to be parallelized on a central processing unit (CPU) and at least one heterogeneous unit of the electronic device among a plurality of job steps of the job based on the profiling information; determining a unit to process each unit data among the CPU and the heterogeneous unit based on the profiling information, with respect to the determined at least one job step; and determining a unit to process each task among the CPU and the heterogeneous unit based on the profiling information, with respect to at least one job step including a plurality of separately executable tasks in the determined at least one job step.

Abstract: A parallelization method includes: obtaining profiling information for each job step of a job by performing profiling of the job to be executed on an electronic device; determining at least one job step to be parallelized on a central processing unit (CPU) and at least one heterogeneous unit of the electronic device among a plurality of job steps of the job based on the profiling information; determining a unit to process each unit data among the CPU and the heterogeneous unit based on the profiling information, with respect to the determined at least one job step; and determining a unit to process each task among the CPU and the heterogeneous unit based on the profiling information, with respect to at least one job step including a plurality of separately executable tasks in the determined at least one job step.