Open Space Labs, Inc.

Michael Ben Fleischman

Jeevan James Kalanithi

Philip DeCamp

Thomas Friel Allen

Gabriel F Hein

Elliott St George Wilson Kember

Abraham Botros

A spatial indexing system receives a sequence of images depicting an environment, such as a floor of a construction site, and performs a spatial indexing process to automatically identify the spatial locations at which each of the images were captured. The spatial indexing system also generates an immersive model of the environment and provides a visualization interface that allows a user to view each of the images at its corresponding location within the model.

Automated spatial indexing of images based on floorplan features

A spatial indexing system receives a video that is a sequence of frames depicting an environment, such as a floor of a construction site, and performs a spatial indexing process to automatically identify the spatial locations at which each of the images were captured. The spatial indexing system also generates an immersive model of the environment and provides a visualization interface that allows a user to view each of the images at its corresponding location within the model.

Automated spatial indexing of images to video

A system aligns a 3D model of an environment with image frames of the environment and generates a visualization interface that displays a portion of the 3D model and a corresponding image frame. The system receives LIDAR data collected in the environment and generates a 3D model based on the LIDAR data. For each image frame, the system aligns the image frame with the 3D model. After aligning the image frames with the 3D model, when the system presents a portion of the 3D model in an interface, it also presents an image frame that corresponds to the portion of the 3D model.

Rendering depth-based three-dimensional model with integrated image frames

A system automatically identifies objects in an environment based on a walkthrough video and an annotated floorplan of the environment. The annotated floorplan indicates locations and types of objects that are expected to be in the environment. The system receives the walkthrough video and generates a 3D model of the environment. The system applies a machine learning model to the walkthrough video to identify regions within frames where objects are captured. After identifying the regions within frames of the walkthrough video that include objects, the system modifies corresponding regions of the 3D model to include the identified objects. For each of the identified objects, the system determines a likelihood of the identified object being present at a location in the environment based on a comparison of the modified 3D model and the annotated floorplan.

Machine learning based object identification using scaled diagram and three-dimensional model

A spatial indexing system receives a set of walkthrough videos of an environment taken over a period of time and receives an image search query that includes an image of an object. The spatial indexing system searches the set of walkthrough videos for instances of the object. The spatial indexing system presents search results in a user interface, displaying in a first portion a 2D map associated with one walkthrough video with marked locations of instances of the object and a second portion with a histogram of instances of the object over time in the set of walkthrough videos.

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Los Angeles, CA, United States of America

Michael Ben Fleischman