Streem, Inc.

Flora Ponjou Tasse

Pavan Kumar Kamaraju

Ghislain Fouodji Tasse

Sean M Adkinson

Ryan R Fink

Nikilesh Urella

Embodiments include systems and methods for creation of a 3D mesh from a video stream or a sequence of frames. A sparse point cloud is first created from the video stream, which is then densified per frame by comparison with spatially proximate frames. A 3D mesh is then created from the densified depth maps, and the mesh is textured by projecting the images from the video stream or sequence of frames onto the mesh. Metric scale of the depth maps may be estimated where direct measurements are not able to be measured or calculated using a machine learning depth estimation network.

3D model reconstruction and scale estimation

Frames for texturing a 3D mesh may be selected to minimize the number of frames required to completely texture the mesh, thus reducing the overhead of texturing. Keyframes are selected from a video stream on the basis of amount of overlap from previously selected keyframes, with the amount of overlap held below a predetermined threshold. The 3D mesh may also be refined and corrected to ensure a higher quality mesh application, including color correction of the selected keyframes. Other embodiments are described.

Efficient texture mapping of a 3-D mesh

Real-time local live texturing of a 3D mesh includes adding keyframes that partially overlap in a sequence of frames to a queue of keyframes. When changes to a property of the 3D mesh created from the sequence of frames meet a predetermined threshold, the face vertices are project into RGB images of the keyframes to test visibility, and the keyframes from which the face is visible is added to a visible keyframe list for each of the faces. A most recently added keyframe from the queue of keyframes is assigned to each face in the 3D mesh, and the 3D mesh is divided into mesh segments based on the assigned keyframes. The keyframe assigned to each of the mesh segments is used to compute texture coordinates for vertices in the respective mesh segment, and an image in the keyframe is assigned as a texture for the respective mesh segment. Colors from the visible keyframe list associated with each of the vertices are averaged into a single RGB value, and the RGB value is assigned to the vertex.

Local live texturing of a 3D mesh

The present invention relates to methods for searching for two-dimensional or three-dimensional objects. More particularly, the present invention relates to searching for two-dimensional or three-dimensional objects in a collection by using a multi-modal query of image and/or tag data. Aspects and/or embodiments seek to provide a method of searching for digital objects using any combination of images, three-dimensional shapes and text by embedding the vector representations for these multiple modes in the same space. Aspects and/or embodiments can be easily extensible to any other type of modality, making it more general.

Multi-modal image search

Embodiments include systems and methods for generating a 3D mesh from a video stream or other image captured contemporaneously with AR data. The AR data is used to create a depth map, which is then fused with images from frames of the video to form a full 3D mesh. The images and depth map can also be used with an object detection algorithm to recognize 3D objects within the 3D mesh. Methods for fingerprinting the video with AR data captured contemporaneously with each frame are disclosed.

Capture of raw augmented reality data and subsequent 3D mesh generation

Methods and systems disclosed herein are directed to detection and recognition of items of data on labels applied to equipment and identifying metadata labels for the items of data using NLP. Embodiments may include identifying one or more items of data on an image of a label associated with a piece of equipment, determining, using NLP on the one or more items of data of the image, one or more metadata associated, respectively, with the identified one or more items of data, and outputting at least one of the one or more metadata and associated items of data.

Extracting data and metadata from identification labels using natural language processing

Embodiments include systems and methods for determining a 6D pose estimate associated with an image of a physical 3D object captured in a video stream. An initial 6D pose estimate is inferred and then further iteratively refined. The video stream may be frozen to allow the user to tap or touch a display to indicate a location of the user-input keypoints. The resulting 6D pose estimate is used to assist in replacing or superimposing the physical 3D object with digital or virtual content in an augmented reality (AR) frame.

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London, United Kingdom

Flora Ponjou Tasse