3D Reconstruction Pose Error Thresholding
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Solution Overview
Problem
Existing 3D reconstruction methods for mobile devices are computationally expensive and resource-demanding, particularly when real-time SLAM algorithms provide inaccurate pose estimates, making them unsuitable for real-time navigation and augmented reality applications.
Innovation Solution
A method where a mobile device performs lightweight 3D reconstruction when pose errors are within a certain threshold, and sends a 3D reconstruction request to a server for more accurate central 3D reconstruction when pose errors exceed the threshold, allowing for a balance between energy efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If computationally expensive 3D reconstruction algorithms are used to improve accuracy, then manufacturing precision is improved, but use of energy increases
Solution Approach 1:
The patent changes the parameter of reconstruction responsibility by dynamically switching between device-based reconstruction (for small pose errors) and server-based reconstruction (for large pose errors). This parameter change allows the system to adapt computational resource allocation based on real-time pose estimation quality, reducing energy consumption while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary mechanism (the error threshold comparison and decision logic) that mediates between the mobile device's computational resources and the server's computational resources. This intermediary determines when to offload reconstruction tasks to the server, balancing energy efficiency with reconstruction accuracy.
2Measurement precision
If computationally expensive SLAM pose refinement is performed to improve pose accuracy, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent changes the parameter of pose refinement responsibility by switching between device-based refinement (when pose error is within threshold) and server-based refinement (when pose error exceeds threshold). This dynamic parameter change avoids unnecessary energy-consuming refinement operations on the device while maintaining measurement precision when needed.
Solution Approach 2:
The mobile device performs self-service by autonomously evaluating its own pose error and making decisions about whether to perform reconstruction locally or request server assistance. This self-service mechanism eliminates the need for continuous expensive refinement while maintaining adequate accuracy through intelligent task allocation.
3Productivity
If real-time 3D reconstruction is performed on mobile device to improve speed, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the 3D reconstruction task into two parts: initial reconstruction and refinement reconstruction. The mobile device performs initial reconstruction locally for speed, while server-based refinement is performed selectively when pose errors are large. This segmentation allows real-time performance while reducing device complexity by offloading only necessary refinement tasks.
Solution Approach 2:
The patent introduces dynamic adaptability by making the reconstruction process responsive to real-time pose error conditions. The system dynamically adjusts computational complexity based on whether pose errors are within or exceed the threshold, allowing the device to maintain real-time performance while managing computational resource requirements flexibly.
Data Source
AI summary
It is provided a method for performing 3D reconstruction. The method includes: obtaining sensor data; determining a pose estimate; estimating a pose error; comparing the pose error against an error threshold; performing a device 3D reconstruction when the pose error is determined to be smaller than the error threshold, resulting in updates to a device 3D model; sending a 3D reconstruction request to the server to perform a central 3D reconstruction, when the pose error is determined to be greater than the error threshold, wherein the 3D reconstruction request includes data based on the sensor data; receiving a result of a central 3D reconstruction from the server; and performing a 3D model fusion of a device 3D model in the mobile device and the result of the central 3D reconstruction, wherein the device 3D model, at least partly, is a result of previous device 3D reconstruction.