3D Semantic Mesh Graph for Object Relationship Estimation
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Solution Overview
Problem
Existing computer vision techniques struggle to determine relationships between objects in physical environments, such as the position of a vase relative to a table or the orientation of a chair in relation to a table.
Innovation Solution
The implementation of systems and methods that utilize a semantic mesh to represent the 3D geometry of a physical environment, where machine learning models can estimate relationships between objects by transforming the semantic mesh into a graph and analyzing node connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision techniques are used to identify physical objects, then object identification capability is improved, but relationship determination capability deteriorates
Solution Approach 1:
The patent segments the semantic mesh into vertices, edges, and faces, where vertices represent object parts, edges represent relationships between parts, and faces represent spatial relationships between objects. This segmentation allows the system to preserve relationship information while maintaining object identification capability.
Solution Approach 2:
The patent transforms 2D image data into a 3D semantic mesh, adding spatial dimensionality that enables relationship determination. By representing objects in 3D space with labeled vertices and faces, the system can infer relationships such as 'on top of', 'next to', and 'facing' that are not present in 2D images alone.
2Measurement precision
If a semantic mesh with many vertices and faces is generated, then representation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential information from the semantic mesh (vertices, edges, and faces with semantic labels) and represents it as a graph structure. This extraction reduces computational complexity by removing redundant information while preserving the essential spatial and semantic relationships needed for object relationship determination.
Solution Approach 2:
The patent creates a simplified graph representation that copies only the necessary structural and semantic information from the complex semantic mesh. The graph uses nodes for objects and edges for relationships, providing a compressed representation that maintains accuracy while reducing computational burden.
3Measurement precision
If machine learning models process detailed graph representations, then relationship estimation accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by processing only the essential graph features (nodes and edges with semantic labels) rather than the complete semantic mesh. This selective processing maintains relationship estimation accuracy while significantly reducing processing time by focusing computational resources on the most informative elements.
Data Source
AI summary
Implementations disclosed herein provide systems and methods that determine relationships between objects based on an original semantic mesh of vertices and faces that represent the 3D geometry of a physical environment. Such an original semantic mesh may be generated and used to provide input to a machine learning model that estimates relationships between the objects in the physical environment. For example, the machine learning model may output a graph of nodes and edges indicating that a vase is on top of a table or that a particular instance of a vase, V1, is on top of a particular instance of a table, T1.