RECONSTRUCTING THREE-DIMENSIONAL SCENES IN A TARGET COORDINATE SYSTEM FROM MULTIPLE VIEWS

    公开(公告)号:US20210295606A1

    公开(公告)日:2021-09-23

    申请号:US16822819

    申请日:2020-03-18

    Applicant: Adobe Inc.

    Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for reconstructing three-dimensional meshes from two-dimensional images of objects with automatic coordinate system alignment. For example, the disclosed system can generate feature vectors for a plurality of images having different views of an object. The disclosed system can process the feature vectors to generate coordinate-aligned feature vectors aligned with a coordinate system associated with an image. The disclosed system can generate a combined feature vector from the feature vectors aligned to the coordinate system. Additionally, the disclosed system can then generate a three-dimensional mesh representing the object from the combined feature vector.

    Intuitive editing of three-dimensional models

    公开(公告)号:US10957117B2

    公开(公告)日:2021-03-23

    申请号:US16204980

    申请日:2018-11-29

    Applicant: ADOBE INC.

    Abstract: Embodiments of the present invention are directed towards intuitive editing of three-dimensional models. In embodiments, salient geometric features associated with a three-dimensional model defining an object are identified. Thereafter, feature attributes associated with the salient geometric features are identified. A feature set including a plurality of salient geometric features related to one another is generated based on the determined feature attributes (e.g., properties, relationships, distances). An editing handle can then be generated and displayed for the feature set enabling each of the salient geometric features within the feature set to be edited in accordance with a manipulation of the editing handle. The editing handle can be displayed in association with one of the salient geometric features of the feature set.

    Realistically illuminated virtual objects embedded within immersive environments

    公开(公告)号:US10950038B2

    公开(公告)日:2021-03-16

    申请号:US16800783

    申请日:2020-02-25

    Applicant: ADOBE INC.

    Abstract: Matching an illumination of an embedded virtual object (VO) with current environment illumination conditions provides an enhanced immersive experience to a user. To match the VO and environment illuminations, illumination basis functions are determined based on preprocessing image data, captured as a first combination of intensities of direct illumination sources illuminates the environment. Each basis function corresponds to one of the direct illumination sources. During the capture of runtime image data, a second combination of intensities illuminates the environment. An illumination-weighting vector is determined based on the runtime image data. The determination of the weighting vector accounts for indirect illumination sources, such as surface reflections. The weighting vector encodes a superposition of the basis functions that corresponds to the second combination of intensities. The method illuminates the VO based on the weighting vector. The resulting illumination of the VO matches the second combination of the intensities and surface reflections.

    Reconstructing three-dimensional scenes using multi-view cycle projection

    公开(公告)号:US10937237B1

    公开(公告)日:2021-03-02

    申请号:US16816080

    申请日:2020-03-11

    Applicant: Adobe Inc.

    Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for reconstructing three-dimensional object meshes from two-dimensional images of objects using multi-view cycle projection. For example, the disclosed system can determine a multi-view cycle projection loss across a plurality of images of an object via an estimated three-dimensional object mesh of the object. For example, the disclosed system uses a pixel mapping neural network to project a sampled pixel location across a plurality of images of an object and via a three-dimensional mesh representing the object. The disclosed system determines a multi-view cycle consistency loss based on a difference between the sampled pixel location and a cycle projection of the sampled pixel location and uses the loss to update the pixel mapping neural network, a latent vector representing the object, or a shape generation neural network that uses the latent vector to generate the object mesh.

    MIXING SEGMENTATION ALGORITHMS UTILIZING SOFT CLASSIFICATIONS TO IDENTIFY SEGMENTS OF THREE-DIMENSIONAL DIGITAL MODELS

    公开(公告)号:US20200320715A1

    公开(公告)日:2020-10-08

    申请号:US16907663

    申请日:2020-06-22

    Applicant: ADOBE INC.

    Abstract: The present disclosure includes methods and systems for identifying and manipulating a segment of a three-dimensional digital model based on soft classification of the three-dimensional digital model. In particular, one or more embodiments of the disclosed systems and methods identify a soft classification of a digital model and utilize the soft classification to tune segmentation algorithms. For example, the disclosed systems and methods can utilize a soft classification to select a segmentation algorithm from a plurality of segmentation algorithms, to combine segmentation parameters from a plurality of segmentation algorithms, and/or to identify input parameters for a segmentation algorithm. The disclosed systems and methods can utilize the tuned segmentation algorithms to accurately and efficiently identify a segment of a three-dimensional digital model.

    Three-dimensional segmentation of digital models utilizing soft classification geometric tuning

    公开(公告)号:US10706554B2

    公开(公告)日:2020-07-07

    申请号:US15487813

    申请日:2017-04-14

    Applicant: Adobe Inc.

    Abstract: The present disclosure includes methods and systems for identifying and manipulating a segment of a three-dimensional digital model based on soft classification of the three-dimensional digital model. In particular, one or more embodiments of the disclosed systems and methods identify a soft classification of a digital model and utilize the soft classification to tune segmentation algorithms. For example, the disclosed systems and methods can utilize a soft classification to select a segmentation algorithm from a plurality of segmentation algorithms, to combine segmentation parameters from a plurality of segmentation algorithms, and/or to identify input parameters for a segmentation algorithm. The disclosed systems and methods can utilize the tuned segmentation algorithms to accurately and efficiently identify a segment of a three-dimensional digital model.

    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.

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