3D Mesh Splitting for Remote XR Environment Analysis
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Solution Overview
Problem
Current IT environments face challenges in efficiently curating, searching, and analyzing massive quantities of diverse data, particularly in remote collaboration for extended reality environments, where users struggle to interact with real-world environments due to limited views and restricted analysis capabilities.
Innovation Solution
An event-based data intake and query system, such as the SPLUNK® ENTERPRISE system, is used to collect, index, and search machine data from various sources, employing a flexible schema and late-binding schema to facilitate efficient data retrieval and analysis, enabling users to search all machine data instead of pre-specified sets, and allowing for the extraction of insights from diverse data formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If video streaming is used to share real-world environment with remote users, then remote users can access the environment, but their analysis capability is limited by the host's view
Solution Approach 1:
The system creates a digital 3D copy (mesh) of the physical environment that can be independently manipulated and explored by remote users. Instead of streaming a fixed video view, the system captures spatial data and generates a replicable digital twin that preserves all environmental details, allowing remote users to analyze the complete environment without being constrained by the host's perspective.
2Adaptability or versatility
If all machine data is made searchable, then data analysis flexibility is improved, but data curation and processing complexity increases
Solution Approach 1:
The system performs preliminary indexing and structuring of machine data during the data intake phase, organizing data into searchable formats before users need to query it. By pre-processing and categorizing data with appropriate schemas and metadata, the system enables flexible ad-hoc searching without requiring complex processing at query time, thus balancing versatility with manageable complexity.
Data Source
AI summary
Various implementations or examples set forth a method for scanning a three-dimensional (3D) environment. The method includes generating, based on sensor data captured by a depth sensor on a device, a 3D mesh representing a physical space; dividing the 3D mesh into a plurality of sub-meshes, wherein each of the plurality of sub-meshes comprises a corresponding set of vertices and a corresponding set of faces comprising edges between pairs of vertices; determining that at least a portion of a first sub-mesh in the plurality of sub-meshes is in a current frame captured by an image sensor on the device; and updating the 3D mesh by texturing the at least a portion of the first sub-mesh with one or more pixels in the current frame onto which the first sub-mesh is projected.