Abstract:
A processor-implemented method includes: extracting pyramid level color feature maps from two or more images; extracting pyramid level density feature maps based on a cost volume generated based on the color feature maps; generating neural scene representation (NSR) cube information representing a three-dimensional (3D) space based on the color feature maps and the density feature maps; and generating a two-dimensional (2D) scene of a field of view (FOV) different from a FOV of the two or more images based on the NSR cube information.
Abstract:
An operational amplifying circuit are provided. The operational amplifying circuit includes a control circuit, pull-up and pull-down transistors, first and second bias circuits, and a bias voltage generating circuit. The control circuit includes first and second input terminals, and is configured to change, when an input voltage transitions to a first level, a voltage level of a pull-up node and a pull-down node to a second level different from the first level. The pull-up transistor provides a power supply voltage to the output terminal. The pull-down transistor connects the output terminal to a ground voltage. The first bias circuit provides a first bias current to the control circuit. The bias voltage generating circuit generates a bias voltage when the voltage level of at least one of the pull-up and pull-down nodes reaches a threshold voltage level, and the second bias circuit provides a second bias current to the control circuit.
Abstract:
A smart card may include data storage and transmission circuitry, a plurality of voltage controllers to supply operational power to card circuitry, a plurality of oscillators to supply an internal clock for the card, and power management circuitry. The power management circuitry may be configured to shut down the oscillators and at least one, but not all, voltage controllers during a period after a data transmission is completed.
Abstract:
A method of determining an illumination pattern includes constructing a dataset by estimating a first surface normal vector of a three-dimensional (3D) object from a first image obtained by capturing the 3D object of which surface normal information is known, the dataset including basis images of the 3D object; generating simulation images in which virtual illumination patterns, obtained based on a combination of the basis images, are applied to the 3D object; estimating a second surface normal vector of the 3D object, by reconstructing a surface normal using a photometric stereo technique based on the virtual illumination patterns and simulation images corresponding to the virtual illumination patterns; and training a neural network to determine an illumination pattern based on a difference between the first surface normal vector and the second surface normal vector.