Collaborative XR Object Scanning With Passive 3D Capture
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Solution Overview
Problem
Existing 3D object capture methods in XR environments are cumbersome, time-consuming, and inefficient, particularly in dynamic scenarios, and require significant user interaction, while technologies like NeRF consume excessive computational resources and battery power.
Innovation Solution
A passive 3D scanning method that operates as a background process in XR devices, allowing objects to be captured without user focus, utilizing cloud processing for initial high-resolution data and model quantization to reduce computational load and battery consumption, with collaborative scanning enabled by multiple user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If photogrammetry is used for 3D object capture, then detailed 3D models can be generated, but the process becomes time-consuming and requires significant user interaction
Solution Approach 1:
The system performs preliminary actions by automatically detecting objects of interest and pre-processing scan data in the background before user interaction is required. The control circuitry identifies objects and prepares initial 3D models autonomously, so when users do interact, the heavy lifting is already done, reducing both time and effort required.
Solution Approach 2:
The system serves itself by automatically detecting objects, initiating scans, and processing data without requiring user initiation or continuous interaction. The control circuitry autonomously manages the entire 3D capture workflow, from object detection to model generation, freeing users from manual scanning operations.
2Measurement precision
If NeRF is used for 3D object capture, then high-resolution models can be generated, but excessive computational resources and battery power are consumed
Solution Approach 1:
The system applies partial action by using simplified scanning methods for initial model creation and reserve detailed NeRF processing only when specifically needed. The control circuitry determines the appropriate level of processing based on user needs and device resources, avoiding excessive computational effort while maintaining the option for high-resolution output when required.
Solution Approach 2:
The system changes processing parameters dynamically based on available computational resources and battery status. The control circuitry adjusts the complexity of 3D reconstruction algorithms in real-time, switching between lightweight methods and more intensive NeRF processing depending on device state, thus optimizing energy consumption while maintaining model quality.
3Ease of operation
If manual scanning initiation is used, then user control over the scanning process is maintained, but the process requires continuous user attention and effort
Solution Approach 1:
The system performs preliminary detection and preparation actions automatically before user interaction is needed. The control circuitry identifies objects of interest and prepares scan data in advance, so when users do engage with the system, they are working with pre-processed information, reducing the effort and attention required during the actual scanning process.
4Measurement precision
If traditional scanning methods are used in dynamic environments, then complete object capture is attempted, but the process becomes cumbersome and may miss objects that move or are briefly encountered
Solution Approach 1:
The system serves itself by automatically detecting objects, initiating scans, and managing the capture process without requiring user coordination or attention. The control circuitry autonomously handles dynamic environments by continuously monitoring for objects of interest and initiating appropriate scanning sequences, making the system adaptable to moving objects without adding operational complexity for users.
Data Source
AI summary
There are provided systems and methods for scanning objects in a virtual environment. In particular, the present disclosure pertains to the domain of three-dimensional (3D) object capturing in extended reality (XR) environments and to an optimized system and method for passively capturing 3D objects within Extended Reality (XR) environments. A selection of an object from within an XR environment by a user via a user device is detected. A 3D capture session is initiated based on the detection. Scan data corresponding to the selected object is captured during the 3D capture session, where the capturing is performed as a background process on the user device. A 3D representation or model of the object is generated in the XR environment and provided to the user via the user device.