Semantic 3D Mesh Segmentation for Selective Remote Editing
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Solution Overview
Problem
Existing 3D modeling and augmented reality technologies struggle to efficiently segment and modify individual objects within physical environments, limiting the ability to accurately and intuitively manipulate and update 3D models.
Innovation Solution
Utilizing deep learning techniques, particularly neural networks, to semantically segment 3D meshes, allowing for the identification and labeling of objects within 3D models, enabling precise modification and manipulation by service professionals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional 3D modeling algorithms are used to detect features and create 3D meshes, then the system can capture physical environments, but it struggles to efficiently segment and modify individual objects within the 3D model
Solution Approach 1:
The patent applies semantic segmentation to divide the 3D mesh into distinct object segments, allowing individual objects to be identified and modified independently. This enables service professionals to select and edit specific objects (e.g., fixtures, furniture) without affecting other elements in the 3D model, directly improving ease of operation for object modification.
Solution Approach 2:
The patent introduces an intermediary service professional who receives the semantically segmented 3D model, performs modifications based on customer requests, and returns the updated model. This intermediary layer enables complex modifications to be performed remotely and efficiently, improving productivity while maintaining ease of operation through specialized expertise.
2Measurement precision
If deep learning techniques are used to semantically segment 3D meshes, then object identification precision improves, but computational complexity and processing time increase
Solution Approach 1:
The patent performs semantic segmentation as a preliminary step before modification, creating a pre-processed 3D model with identified objects and boundaries. This preliminary action enables subsequent modifications to be performed more efficiently, as the system already has object information prepared, reducing the complexity of real-time processing during the modification phase.
3Adaptability or versatility
If the entire 3D model is transmitted for modification, then all objects can be edited, but data transmission volume and processing time increase
Solution Approach 1:
The patent extracts and transmits only the specific object or segment that needs modification, rather than the entire 3D model. This extraction approach maintains the ability to modify objects accurately while significantly reducing data transmission volume and processing time, as only the relevant portion of the model is sent to the service professional for editing.
Data Source
AI summary
Embodiments include systems, processes, and/or techniques for creating a semantically segmented 3D mesh of a physical environment, where the semantically segmented 3D mesh may be at least partially created by a first user, and where a second user, for example a service professional, may view and modify one of the segments of the semantically segmented 3D mesh for subsequent viewing by the first user. Other embodiments may be described and/or claimed.