3D Scene Understanding for Augmented Reality Object Placement
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Solution Overview
Problem
Current augmented reality applications face challenges in accurately overlaying or blending virtual content with real-world scenes due to a lack of detailed spatial understanding, particularly in positioning virtual objects within 3D spaces without precise knowledge of object locations and orientations.
Innovation Solution
The use of a smartphone or electronic device equipped with image capture, depth sensing, and inertial measurement units to capture RGB-D data, generate a 3D model of the space, and apply convolutional neural networks to label and segment objects, determining their canonical poses for precise placement of virtual content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual content is overlaid or blended with the real world scene, then augmented reality functionality is achieved, but detailed spatial understanding of the scene is required which increases system complexity
Solution Approach 1:
The system segments the scene understanding task into multiple components: capturing RGB-D data to obtain spatial information, processing depth measurements to identify object boundaries, and determining pose parameters separately for each detected object. This segmentation allows the system to achieve detailed spatial understanding without overwhelming system complexity by breaking down the problem into manageable processing stages.
Solution Approach 2:
The system performs preliminary actions by pre-capturing and storing RGB-D data, depth measurements, and trajectory information before virtual content placement. The device pre-processes this data to generate a structured representation of the real-world scene including object locations and orientations, so that when virtual content needs to be overlaid, the spatial framework is already prepared and ready for integration.
2Measurement precision
If accurate placement of virtual objects is achieved through detailed spatial understanding, then positioning precision is improved, but the difficulty of detecting and measuring object properties increases
Solution Approach 1:
The system introduces an intermediary processing layer that transforms raw RGB-D data and depth measurements into structured pose parameters. The depth sensor acts as an intermediary between the camera and the virtual content placement system, providing intermediate depth information that simplifies the detection process. The system uses this intermediary data to automatically infer object boundaries and orientations without requiring complex direct analysis of the full scene.
3Measurement precision
If multiple sensors and processing units are used to capture and process RGB-D data, then spatial understanding accuracy is improved, but device complexity increases
Solution Approach 1:
The system merges multiple sensor inputs (RGB camera, depth sensor, inertial measurement unit) into a unified data processing pipeline. By combining these sensors and processing their outputs together through integrated algorithms, the system achieves accurate spatial understanding while managing device complexity through unified processing rather than separate independent systems. The merged approach allows shared processing resources and coordinated sensor operation.
Data Source
AI summary
An electronic device is configured to performing a three-dimensional (3D) scan of an interior space. In some cases, the electronic device acquires information and depth measurements relative to the electronic device. The electronic device acquires voxels in a 3D grid that is generated from the 3D scan. The voxels represent portions of the volume of the interior space. The electronic device determines a trajectory and poses of the electronic device concurrently with performing the 3D scan of the interior space. The electronic device labels voxels representing objects in the interior space based on the trajectory and the poses. In some cases, the electronic device uses queries to perform spatial reasoning at an object level of granularity, positions, overlays, or blends virtual objects into an augmented reality representation of the interior space or modifies positions or orientations of the objects by applying a transformation to corresponding connected components.