Bentley Systems, Inc.

Jean-Philippe Pons

Luc Robert

Elenie Godzaridis

Stephane Nullans

Cyril Novel

Stéphane Nullans

A sample may be generated for each point of a plurality of point clouds that represent a scene. A visibility ray may be created between each point of the plurality of point clouds and the one or more sources that generated the point. One or more sample, if any, that intersect a visibility ray may be identified. Each point corresponding to an intersecting sample may be determined to represent or likely represent an unwanted object if the visibility ray is from a different source that did not generate the point and the point is not coherent with any points generated by the different source. A visibility score for each point determined to represent or likely represent an unwanted object may be adjusted. A model may be generated, wherein the model does not include the unwanted object in the scene but includes the permanent object with see-through characteristics in the scene.

Systems, methods, and media for filtering points of a point cloud utilizing visibility factors to generate a model of a scene

In various example embodiments, a variational technique is provided for texturing a polygon mesh based on source images of a scene. The technique combines information from multiple source images to generate a texture map with patches that are the most similar to corresponding patches of the source images. The texture map may be the direct result of an optimization that jointly searches for the texture map and patch correspondence, which is initially unknown. The optimization may use a variational formulation that scores every possible texture map using an energy function that includes a matching term that quantifies how similar patches of the texture map are to the corresponding patches of the source images, and a regularization term that quantifies how complex the patch correspondence is.

Variational technique for texturing a mesh from multiple views

In an example embodiment, techniques are provided for locking a region of fully-connected large-scale multi-dimensional spatial data (e.g., a large-scale 3-D mesh) defined by a bounding box. A region is associated with a lock state (e.g., exclusive or sharable). Clients may access the fully-connected large-scale multi-dimensional spatial data based on a comparison of the bounding box of the requested spatial data to the bounding boxes of other client's locks and their lock state.

Locking of spatial regions of large-scale fully-connected multi-dimensional spatial data for collaborative updating

In an example embodiment, techniques are provided for concurrently editing fully-connected large-scale multi-dimensional spatial data (e.g., a large-scale 3-D mesh) by ensuring that edits performed by multiple clients are non-conflicting edits that are 'trivially' mergeable (e.g. mergeable simply via cut-and-paste operations). Conflicting edits may be prevented by locks (e.g., region-based locks). Non-conflicting edits that require 'non-trivial' merging may be prevented through the use of marked read-only boundaries.

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Le Rouret, France

Jean-Philippe Pons