XR Scene Reconstruction Using Segmented Depth Processing
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Solution Overview
Problem
Extending the functionality of battery-powered devices to support computationally intensive operations for generating detailed depth maps and understanding real-world environments for extended reality (XR) displays is challenging due to resource exhaustion and heat generation, which limits the battery life and processing efficiency.
Innovation Solution
A system and method utilizing a depth sensor, image sensor, inertial measurement unit (IMU), and a controller to perform three-dimensional scene reconstruction, where the controller receives and processes depth and image data to determine an initial six-degree-of-freedom pose, optimizing it for generating a dense depth map, surface mesh, and semantically segmented objects, and renders XR frames with projected virtual objects on real-world surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed depth maps and machine learning models are used for object recognition and scene understanding, then the accuracy and detail of XR display is improved, but the processing power and battery resources are exhausted
Solution Approach 1:
The system segments the scene understanding process into multiple stages: initial coarse segmentation using lightweight methods, followed by selective refinement only in regions where virtual objects need to interact with the real world. This divides the computational workload to maintain detail where needed while reducing overall energy consumption.
Solution Approach 2:
Instead of processing the entire scene at maximum detail, the system applies partial processing by focusing computational resources only on specific regions and objects that are relevant to the current XR interaction. This partial action approach reduces total processing load while maintaining sufficient detail for the application.
2Manufacturing precision
If computationally intensive operations are performed for XR display generation, then the quality of virtual object projection is improved, but heat generation increases
Solution Approach 1:
The system dynamically adjusts the computational intensity and processing quality based on real-time conditions, including device temperature levels. When temperature increases, the system automatically reduces the computational load for scene reconstruction and object recognition, thereby reducing heat generation while maintaining acceptable projection accuracy for the current XR experience.
Solution Approach 2:
The system changes operational parameters such as resolution, processing frequency, and computational precision based on thermal conditions. By adjusting these parameters dynamically, the system maintains projection quality within acceptable ranges while preventing excessive heat buildup that would degrade device performance.
3Reliability
If continuous computationally intensive operations are performed, then the XR display quality is maintained, but battery life is reduced
Solution Approach 1:
Instead of continuous maximum-performance operations, the system uses periodic computation cycles where scene reconstruction and object recognition are updated at optimized intervals. This periodic action maintains XR display quality by refreshing the scene understanding at sufficient rates while allowing lower-power states between updates, thereby extending battery life.
Solution Approach 2:
The system implements adaptive performance tuning that automatically adjusts computational intensity based on usage patterns and device state. By self-regulating the balance between processing quality and power consumption, the system extends battery life without requiring manual user intervention to maintain acceptable XR experience quality.
Data Source
AI summary
A method includes receiving depth data of a real-world scene from a depth sensor, receiving image data of the scene from an image sensor, receiving movement data of the depth and image sensors from an IMU, and determining an initial 6DOF pose of an apparatus based on the depth data, image data, and/or movement data. The method also includes passing the 6DOF pose to a back end to obtain an optimized pose and generating, based on the optimized pose, image data, and depth data, a three-dimensional reconstruction of the scene. The reconstruction includes a dense depth map, a dense surface mesh, and/or one or more semantically segmented objects. The method further includes passing the reconstruction to a front end and rendering, at the front end, an XR frame. The XR frame includes a three-dimensional XR object projected on one or more surfaces of the scene.