Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that apply a resolution independent, vector-based decal on a 3D object. In one or more implementations, the disclosed systems apply piecewise non-linear transformation on an input decal vector geometry to align the decal with a surface of an underlying 3D object. To apply a vector-based decal on a 3D object, in certain embodiments, the disclosed systems parameterize a 3D mesh of the 3D object to create a mesh map. Moreover, in some instances, the disclosed systems determine intersections between edges of a decal geometry and edges of the mesh map to add vertices to the decal geometry at the intersections. Additionally, in some implementations, the disclosed systems lift and project vertices of the decal geometry into three dimensions to align the vertices with faces of the 3D mesh of the 3D object.

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.

Abstract: In implementations of systems for visualizing vector graphics in three-dimensional scenes, a computing device implements a projection system to receive input data describing a digital image depicting a three-dimensional scene and a vector graphic to be projected into the three-dimensional scene. The projection system generates a depth image by estimating disparity values for pixels of the digital image. A three-dimensional mesh is computed that approximates the three-dimensional scene based on the depth image. The projection system projects the vector graphic onto the digital image by transforming the vector graphic based on the three-dimensional mesh.

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that apply a resolution independent, vector-based decal on a 3D object. In one or more implementations, the disclosed systems apply piecewise non-linear transformation on an input decal vector geometry to align the decal with a surface of an underlying 3D object. To apply a vector-based decal on a 3D object, in certain embodiments, the disclosed systems parameterize a 3D mesh of the 3D object to create a mesh map. Moreover, in some instances, the disclosed systems determine intersections between edges of a decal geometry and edges of the mesh map to add vertices to the decal geometry at the intersections. Additionally, in some implementations, the disclosed systems lift and project vertices of the decal geometry into three dimensions to align the vertices with faces of the 3D mesh of the 3D object.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed that utilizes machine learning models for patch retrieval and deformation in completing three-dimensional digital shapes. In particular, in one or more implementations the disclosed systems utilize a machine learning model to predict a coarse completion shape from an incomplete 3D digital shape. The disclosed systems sample coarse 3D patches from the coarse 3D digital shape and learn a shape distance function to retrieve detailed 3D shape patches in the input shape. Moreover, the disclosed systems learn a deformation for each retrieved patch and blending weights to integrate the retrieved patches into a continuous surface.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating digital chain pull paintings in digital images. The disclosed system digitally animates a chain pull painting from a digital drawing path by determining a plurality of digital bead points along the digital drawing path. In response to a movement of one of the digital bead points from a first position to a second position (e.g., based on a pull input performed at a selected digital bead point), the disclosed system determines updated positions of one or more digital bead points along the path. The disclosed system also generates one or more strokes in the digital image from previous positions of the digital bead points to the updated positions of the digital bead points.

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.

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.

Abstract: Techniques are described herein for generating and using a unified shape representation that encompasses features of different types of shape representations. In some embodiments, the unified shape representation is a unicode comprising a vector of embeddings and values for the embeddings. The embedding values are inferred, using a neural network that has been trained on different types of shape representations, based on a first representation of a three-dimensional (3D) shape. The first representation is received as input to the trained neural network and corresponds to a first type of shape representation. At least one embedding has a value dependent on a feature provided by a second type of shape representation and not provided by the first type of shape representation. The value of the at least one embedding is inferred based upon the first representation and in the absence of the second type of shape representation for the 3D shape.

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.

Abstract: Embodiments provide systems, methods, and computer storage media for generating a 3D model from a target 2D image or 3D point cloud (e.g., generated by a 3D scan). Given a particular target, a retrieval network retrieves or identifies a source model from a database, and a deformation network deforms the source model to fit the target. In some cases, joint learning is employed to enable the retrieval and deformation networks to jointly learn a deformation-aware retrieval embedding space and an individualized deformation space for each source model. In some cases, the retrieval network retrieves based on distance in the deformation-aware retrieval embedding space, enabling the retrieval module to retrieve a source model that best fits to the target after deformation. In some cases, a deformation is decomposed into a plurality of per-part deformations, and/or and the retrieval embedding space is used to select training data.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for iteratively decimating a three-dimensional mesh utilizing successive self-parameterization. For example, the disclosed system can self-parameterize local geometries of a three-dimensional mesh using surface mappings within a two-dimensional surface mapping space. The disclosed system can collapse edges in the three-dimensional mesh to create new vertices from the collapsed edges. The disclosed system can parameterize the collapsed edges based on the surface mappings to collapse corresponding edges within the surface mapping space. The disclosed system can thus generate a decimated three-dimensional mesh by collapsing edges in the three-dimensional mesh while providing a bijective map between points in the decimated three-dimensional mesh and corresponding points in the three-dimensional mesh.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for iteratively decimating a three-dimensional mesh utilizing successive self-parameterization. For example, the disclosed system can self-parameterize local geometries of a three-dimensional mesh using surface mappings within a two-dimensional surface mapping space. The disclosed system can collapse edges in the three-dimensional mesh to create new vertices from the collapsed edges. The disclosed system can parameterize the collapsed edges based on the surface mappings to collapse corresponding edges within the surface mapping space. The disclosed system can thus generate a decimated three-dimensional mesh by collapsing edges in the three-dimensional mesh while providing a bijective map between points in the decimated three-dimensional mesh and corresponding points in the three-dimensional mesh.

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.

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.

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.

Abstract: Techniques are described herein for generating and using a unified shape representation that encompasses features of different types of shape representations. In some embodiments, the unified shape representation is a unicode comprising a vector of embeddings and values for the embeddings. The embedding values are inferred, using a neural network that has been trained on different types of shape representations, based on a first representation of a three-dimensional (3D) shape. The first representation is received as input to the trained neural network and corresponds to a first type of shape representation. At least one embedding has a value dependent on a feature provided by a second type of shape representation and not provided by the first type of shape representation. The value of the at least one embedding is inferred based upon the first representation and in the absence of the second type of shape representation for the 3D shape.

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.

Abstract: A transparent optical switch includes network management and performance monitoring using bit level information obtained by extracting selected information on a polling basis and analyzing the extracted information in the electrical domain. In one embodiment, a signal is injected into the switch fabric of the switch via a demultiplexing device. The injected signal is extracted at the output of the switching fabric via an N:1 switch and analyzed by a signal analyzer to verify input to output connections. In another embodiment, an optical switch includes first and second switch fabrics for 1:2 broadcast capability. In a further embodiment, an optical communication system includes a plurality of optical networks and a plurality of optical switches that cooperate to generate unequipped signals and to obtain autonomously switch-to-switch port connectivity information required for auto-topology discovery.

Abstract: A transparent optical switch includes network management and performance monitoring using bit level information obtained by extracting selected information on a polling basis and analyzing the extracted information in the electrical domain. In one embodiment, a signal is injected into the switch fabric of the switch via a demultiplexing device. The injected signal is extracted at the output of the switching fabric via an N:1 switch and analyzed by a signal analyzer to verify input to output connections. In another embodiment, an optical switch includes first and second switch fabrics for 1:2 broadcast capability. In a further embodiment, an optical communication system includes a plurality of optical networks and a plurality of optical switches that cooperate to generate unequipped signals and to obtain autonomously switch-to-switch port connectivity information required for auto-topology discovery.