中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

86 records (took 0.037 seconds)

    Relightable texture for use in rendering an image
    43.
    发明授权
    Relightable texture for use in rendering an image 有权
    可靠的纹理用于渲染图像

    公开(公告)号：US09418473B2

    公开(公告)日：2016-08-16

    申请号：US14016622

    申请日：2013-09-03

    Applicant: Imagination Technologies Limited

    Inventor： James Imber ,  Adrian Hilton

    IPC: G06T15/04 ,  G06T15/60

    CPC classification number: G06T15/60 ,  G06T15/04

    Abstract: A model of a scene of an image (e.g. a frame of a video sequence) is generated from one or more views of the scene captured from one or more different camera viewpoints. An initial texture for applying to the model is derived from the one or more views of the scene. The initial texture is separated into a lighting estimate and a color estimate, which may be orthogonal and which may be processed independently. The lighting estimate is filtered with a high-pass filter to thereby determine shadow regions of the scene which are regions of detailed shadow which are likely to be caused by ambient occlusion in the scene and which are therefore retained when the texture is relit for rendering the image. A shadow-detail estimate (or “dark map”) is provided which indicates one or more shadow regions of the texture which are to remain in shadow when the image is rendered.

    Abstract translation: 从一个或多个不同的摄像机视点捕获的场景的一个或多个视图生成图像场景的模型（例如视频序列的帧）。 用于应用于模型的初始纹理从场景的一个或多个视图导出。 初始纹理被分为照明估计和颜色估计，其可以是正交的并且可以被独立地处理。 用高通滤波器对照明估计进行滤波，从而确定场景的阴影区域，该阴影区域是可能由场景中的环境遮挡引起的详细阴影区域，并且当纹理依赖于渲染 图片。 提供阴影细节估计（或“暗图”），其指示当呈现图像时将保持在阴影中的纹理的一个或多个阴影区域。

    Relightable texture for use in rendering an image
    44.
    发明申请
    Relightable texture for use in rendering an image 有权
    可靠的纹理用于渲染图像

    公开(公告)号：US20150356769A1

    公开(公告)日：2015-12-10

    申请号：US14121221

    申请日：2014-08-13

    Applicant: Imagination Technologies Limited

    Inventor： James Imber ,  Adrian Hilton

    IPC: G06T15/80 ,  G06T15/50 ,  G06T19/20 ,  G06T7/40 ,  G06T7/60

    CPC classification number: G06T15/506 ,  G06T7/11 ,  G06T7/44 ,  G06T7/49 ,  G06T7/90 ,  G06T15/04 ,  G06T15/10 ,  G06T15/50 ,  G06T15/503 ,  G06T15/80 ,  G06T19/20 ,  G06T2207/10004 ,  G06T2207/10016 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/20016 ,  G06T2207/20021 ,  G06T2207/20028

    Abstract: Relightable free-viewpoint rendering allows a novel view of a scene to be rendered and relit based on multiple views of the scene from multiple camera viewpoints. An initial texture can be segmented into materials and an initial coarse colour estimate is determined for each material. Scene geometry is estimated from the captured views of the scene and is used to scale the initial coarse colour estimates relative to each other such that the different materials appear to be lit with a similar irradiance. In this way, a global irradiance function is estimated describing the scene illumination. This provides a starting point for a colour estimate and shading estimate extraction. The shading estimate can be used to fit surface normals to the global irradiance function. The set of surface normals and the colour estimate are stored for subsequent use to allow relighting of the scene.

    Abstract translation: 可靠的自由观点渲染允许基于多个摄像机视点的场景的多个视图来渲染并再现场景的新颖视图。 可以将初始纹理分割成材料，并且为每种材料确定初始粗略颜色估计。 从场景的捕获视图估计场景几何，并且用于相对于彼此缩放初始粗略颜色估计，使得不同的材料似乎以类似的辐照度点亮。 以这种方式，估计描述场景照明的全局辐照度函数。 这提供了颜色估计和阴影估计提取的起点。 阴影估计可用于将表面法线拟合到全局辐照度函数。 存储表面法线和颜色估计的集合用于随后的使用以允许场景的重新点亮。

    IMAGE SIGNAL PROCESSING
    45.
    发明申请
    IMAGE SIGNAL PROCESSING 有权

    公开(公告)号：US20250005718A1

    公开(公告)日：2025-01-02

    申请号：US18757087

    申请日：2024-06-27

    Applicant: Imagination Technologies Limited

    Inventor： James Imber

    IPC: G06T5/60 ,  G06T5/50 ,  G06V10/98

    Abstract: Training apparatus for training a differentiable model of an image signal processor having a pipeline of separate image signal processing functions, includes processors configured to receive a reference image; and train a first differentiable module to perform a first image signal processing function, whilst not training other differentiable modules, by iteratively inputting, to the differentiable model a degraded image signal that represents a known degradation of the reference image, the degradation being related to the first image signal processing function; processing the degraded image signal using the differentiable model to produce a first processed image including using the first differentiable module to perform the first image signal processing function; calculating an error between the first processed image and the reference image; and updating the first image processing function performed by the first differentiable module based on the calculated error without updating the image processing functions performed by other differentiable modules of the differentiable model of the image signal processor.

    Hardware implementation of a deep neural network with variable output data format
    46.
    发明授权
    Hardware implementation of a deep neural network with variable output data format 有权

    公开(公告)号：US12165045B2

    公开(公告)日：2024-12-10

    申请号：US16136553

    申请日：2018-09-20

    Applicant: Imagination Technologies Limited

    Inventor： Chris Martin ,  David Hough ,  Paul Brasnett ,  Cagatay Dikici ,  James Imber ,  Clifford Gibson

    IPC: G06N3/065 ,  G06F1/3234 ,  G06F7/483 ,  G06F7/544 ,  G06N3/04 ,  G06N3/045 ,  G06N3/063

    Abstract: Hardware implementations of DNNs and related methods with a variable output data format. Specifically, in the hardware implementations and methods described herein the hardware implementation is configured to perform one or more hardware passes to implement a DNN wherein during each hardware pass the hardware implementation receives input data for a particular layer, processes that input data in accordance with the particular layer (and optionally one or more subsequent layers), and outputs the processed data in a desired format based on the layer, or layers, that are processed in the particular hardware pass. In particular, when a hardware implementation receives input data to be processed, the hardware implementation also receives information indicating the desired format for the output data of the hardware pass and the hardware implementation is configured to, prior to outputting the processed data convert the output data to the desired format.

    HARDWARE IMPLEMENTATION OF A CONVOLUTIONAL NEURAL NETWORK
    47.
    发明公开
    HARDWARE IMPLEMENTATION OF A CONVOLUTIONAL NEURAL NETWORK 审中-公开

    公开(公告)号：US20240249131A1

    公开(公告)日：2024-07-25

    申请号：US18623450

    申请日：2024-04-01

    Applicant: Imagination Technologies Limited

    Inventor： Clifford Gibson ,  James Imber

    IPC: G06N3/063 ,  G06F7/00 ,  G06F7/544 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08

    CPC classification number: G06N3/063 ,  G06F7/00 ,  G06F7/5443 ,  G06N3/04 ,  G06N3/045 ,  G06N3/08

    Abstract: A method in a hardware implementation of a Convolutional Neural Network (CNN), includes receiving a first subset of data having at least a portion of weight data and at least a portion of input data for a CNN layer and performing, using at least one convolution engine, a convolution of the first subset of data to generate a first partial result; receiving a second subset of data comprising at least a portion of weight data and at least a portion of input data for the CNN layer and performing, using the at least one convolution engine, a convolution of the second subset of data to generate a second partial result; and combining the first partial result and the second partial result to generate at least a portion of convolved data for a layer of the CNN.

    Histogram-Based Per-Layer Data Format Selection for Hardware Implementation of Deep Neural Network
    49.
    发明公开
    Histogram-Based Per-Layer Data Format Selection for Hardware Implementation of Deep Neural Network 审中-公开

    公开(公告)号：US20230186064A1

    公开(公告)日：2023-06-15

    申请号：US18107471

    申请日：2023-02-08

    Applicant: Imagination Technologies Limited

    Inventor： James Imber ,  Cagatay Dikici

    IPC: G06N3/063 ,  G06F7/499 ,  G06F7/544

    CPC classification number: G06N3/063 ,  G06F7/49915 ,  G06F7/5443 ,  G06N3/045

    Abstract: A histogram-based method of selecting a fixed point number format for representing a set of values input to, or output from, a layer of a Deep Neural Network (DNN). The method comprises obtaining a histogram that represents an expected distribution of the set of values of the layer, each bin of the histogram is associated with a frequency value and a representative value in a floating point number format; quantising the representative values according to each of a plurality of potential fixed point number formats; estimating, for each of the plurality of potential fixed point number formats, the total quantisation error based on the frequency values of the histogram and a distance value for each bin that is based on the quantisation of the representative value for that bin; and selecting the fixed point number format associated with the smallest estimated total quantisation error as the optimum fixed point number format for representing the set of values of the layer.

Patent Agency Ranking