Swept volume determination techniques

    公开(公告)号:US11810255B2

    公开(公告)日:2023-11-07

    申请号:US17333147

    申请日:2021-05-28

    Applicant: Adobe Inc.

    Abstract: Techniques for determining a swept volume of an object moving along a trajectory in a 3D space are disclosed. In some examples, a computer graphics application accesses a representation of the object, such as the signed distance field (SDF), and the trajectory information describing the movement path in the 3D space over a time period. The 3D space is represented using a grid of voxels each having multiple vertices. The computer graphics application determines the swept volume of the object in the 3D space by evaluating a subset of the grid of voxels (e.g., the voxels surrounding the surface of the swept volume). The number of voxels in the subset of voxels is less than the number of voxels in the grid of voxels. The computer graphics application further generates a representation of the swept volume surface for output.

    Generating developable depth images using rank minimization

    公开(公告)号:US11080819B1

    公开(公告)日:2021-08-03

    申请号:US16923936

    申请日:2020-07-08

    Applicant: ADOBE INC.

    Abstract: An image processing system receives an input depth image with a surface that is not developable and generates an output depth image with a piecewise developable surface that approximates the input depth image. Height values for the output depth image are determined using an optimization problem that balances data fidelity and developability. Data fidelity is based on minimizing differences in height values of pixels in the output depth image and height values of pixels in the input depth image. Developability is based on rank minimization of Hessians computed for pixels in the output depth image. In some configurations, the optimization problem is formulated as a semi-definite programming problem and solved using a tailor-made alternating direction method of multipliers algorithm.

    Three-Dimensional Mesh Segmentation

    公开(公告)号:US20210142561A1

    公开(公告)日:2021-05-13

    申请号:US16681693

    申请日:2019-11-12

    Applicant: Adobe Inc.

    Inventor: Noam Aigerman

    Abstract: Three-dimensional (3D) mesh segmentation techniques are described. In one example, a geometry segmentation system determines a vertex direction for each vertex in a 3D mesh. A segment generation module is then employed to generate segments (e.g., as developable geometries) from the 3D mesh. To do so, a vertex selection module selects an initial vertex having an associated vertex direction. A face identification module then identifies a face in the 3D mesh using that initial vertex and at least one other vertex. A segment determination module compares the vertex direction associated with the initial vertex with a normal determined for the face. If the vertex direction is orthogonal to the normal (e.g., within a threshold amount), the face is added to the segment, and sets another one of the vertices of the face as the initial vertex and the process repeats.

    PROGRESSIVELY GENERATING FINE POLYGON MESHES

    公开(公告)号:US20250029335A1

    公开(公告)日:2025-01-23

    申请号:US18355995

    申请日:2023-07-20

    Applicant: Adobe Inc.

    Abstract: In implementation of techniques for progressively generating fine polygon meshes, a computing device implements a mesh progression system to receive a coarse polygon mesh. The mesh progression system generates a fine polygon mesh that has a higher level of resolution than the coarse polygon mesh by decoding the coarse polygon mesh using a machine learning model. The mesh progression system then receives additional data describing a residual feature of a polygon mesh. Based on the additional data, the mesh progression system generates an adjusted fine polygon mesh that has a higher level of resolution than the fine polygon mesh.

    Subdividing a three-dimensional mesh utilizing a neural network

    公开(公告)号:US12118669B2

    公开(公告)日:2024-10-15

    申请号:US17821704

    申请日:2022-08-23

    Applicant: Adobe Inc.

    CPC classification number: G06T17/20 G06N3/02 G06N3/08 G06T7/13 G06T2207/20081

    Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing one or more neural networks to recursively subdivide a three-dimensional mesh according to local geometries of vertices in the three-dimensional mesh. For example, the disclosed system can determine a local geometry (e.g., a one-ring neighborhood of half-flaps) for each vertex in a three-dimensional mesh. For each subdivision iteration, the disclosed system can then utilize a neural network to determine displacement coordinates for existing vertices in the three-dimensional mesh and coordinates for new vertices added to edges between the existing vertices in the three-dimensional mesh in accordance with the local geometries of the existing vertices. Furthermore, the disclosed system can generate a subdivided three-dimensional mesh based on the determined displacement coordinates for the existing vertices and the determined coordinates for the new vertices.

    NEURAL NETWORK BASED 3D OBJECT SURFACE MAPPING

    公开(公告)号:US20230169714A1

    公开(公告)日:2023-06-01

    申请号:US17537343

    申请日:2021-11-29

    CPC classification number: G06T15/04 G06T17/20 G06N3/0454 G06N3/08

    Abstract: Certain aspects and features of this disclosure relate to neural network based 3D object surface mapping. In one example, a first representation of a first surface of a first 3D object and a second representation of a second surface of a second 3D object are produced. A surface mapping function is generated for mapping the first surface to the second surface. The surface mapping function is defined the representations and by a neural network model configured to map a first 2D representation of the first surface to a second 2D representation of the second surface. One or more features of the a first 3D mesh on the first surface can be applied to a second 3D mesh on the second surface using the surface mapping function to produce a modified second surface, which can be rendered through a user interface.

    Three-dimensional mesh segmentation

    公开(公告)号:US11127205B2

    公开(公告)日:2021-09-21

    申请号:US16681693

    申请日:2019-11-12

    Applicant: Adobe Inc.

    Inventor: Noam Aigerman

    Abstract: Three-dimensional (3D) mesh segmentation techniques are described. In one example, a geometry segmentation system determines a vertex direction for each vertex in a 3D mesh. A segment generation module is then employed to generate segments (e.g., as developable geometries) from the 3D mesh. To do so, a vertex selection module selects an initial vertex having an associated vertex direction. A face identification module then identifies a face in the 3D mesh using that initial vertex and at least one other vertex. A segment determination module compares the vertex direction associated with the initial vertex with a normal determined for the face. If the vertex direction is orthogonal to the normal (e.g., within a threshold amount), the face is added to the segment, and sets another one of the vertices of the face as the initial vertex and the process repeats.

    LEARNING HYBRID (SURFACE-BASED AND VOLUME-BASED) SHAPE REPRESENTATION

    公开(公告)号:US20210264659A1

    公开(公告)日:2021-08-26

    申请号:US16799664

    申请日:2020-02-24

    Applicant: Adobe Inc.

    Abstract: Certain embodiments involve techniques for generating a 3D representation based on a provided 2D image of an object. An image generation system receives the 2D image representation and generates a multi-dimensional vector of the input that represents the image. The image generation system samples a set of points and provides the set of points and the multi-dimensional vector to a neural network that was trained to predict a 3D surface representing the image such that the 3D surface is consistent with a 3D surface of the object calculated using an implicit function for representing the image. The neural network predicts, based on the multi-dimensional vector and the set of points, the 3D surface representing the object.

Patent Agency Ranking