Passive 3D Object Capture in XR Environments
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Solution Overview
Problem
Existing 3D object capture methods in XR environments are cumbersome, requiring significant user interaction and computational resources, which leads to inefficiencies and battery drain.
Innovation Solution
A method for passively capturing 3D objects in XR environments by detecting the object's presence and initiating a background 3D scan, allowing for continuous data capture without user focus, and optimizing computational efficiency through cloud processing and spatial segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional photogrammetry methods are used for 3D object capture, then measurement precision can be achieved, but user interaction complexity and time consumption increase significantly
Solution Approach 1:
The system performs automatic object detection and initiation of 3D scanning without requiring user manual triggering. The XR device autonomously detects objects in the field of view and starts the scanning process automatically, eliminating the need for users to manually initiate or focus on the scanning operation while maintaining accurate 3D model generation
Solution Approach 2:
The system performs preliminary object detection and classification before initiating the full 3D scanning process. By pre-identifying objects of interest and determining appropriate scanning parameters in advance, the system prepares the scanning sequence to minimize user intervention while ensuring measurement precision is achieved
2Manufacturing precision
If NeRF techniques are used for high-resolution 3D modeling, then manufacturing precision improves, but computational resources and energy consumption increase excessively
Solution Approach 1:
The system dynamically adjusts the computational intensity and scanning resolution based on real-time conditions, object characteristics, and available computational resources. The scanning process adapts its complexity level, using higher-resolution NeRF techniques only when necessary and switching to more efficient processing methods when battery capacity or computational resources are limited, thus balancing model precision with energy consumption
Solution Approach 2:
The system changes key parameters such as scanning resolution, computational complexity, and data processing intensity based on device battery level, network connectivity status, and object characteristics. By adjusting these parameters dynamically, the system maintains high-resolution modeling capability when resources are available while reducing computational load and battery consumption when constraints exist
3Productivity
If continuous background scanning is performed, then productivity increases, but heat generation and battery drain increase
Solution Approach 1:
Instead of continuous scanning, the system employs periodic scanning triggered by object detection events, user proximity detection, or environmental changes. The scanning operation is activated periodically based on detected conditions and deactivated when objects leave the field of view or scanning completion is confirmed, maintaining productivity while significantly reducing continuous energy consumption and heat generation
Solution Approach 2:
The system extracts and processes only the essential scanning data required for 3D model generation, separating the scanning function from continuous operation. By extracting only necessary scan data during relevant periods and using efficient compression algorithms, the system minimizes data processing load and associated energy consumption while maintaining high productivity in active scanning phases
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.