Natural Language 3D Object Placement System
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Solution Overview
Problem
The placement of objects in 3D virtual environments for AI algorithm development and sensor testing is a time-consuming manual process, requiring specialized skills and not scalable for large numbers of objects, as it involves specifying coordinates and ensuring objects do not interfere with other elements, such as not passing through walls.
Innovation Solution
A system that allows natural language expressions to specify object locations, parsing these expressions into 6D pose specifications, using a parser to isolate translational and rotational constraints, and interpreters to determine valid positions and orientations within the 3D environment, reducing user involvement and expertise needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual coordinate specification and placement verification is used, then placement accuracy is ensured, but productivity is reduced and time consumption increases
Solution Approach 1:
The patent replaces the manual mechanical process of coordinate specification and placement verification with an automated computer-based system. The system automatically calculates valid placement positions by processing environmental data and constraint conditions, eliminating the need for manual coordinate input and verification while maintaining placement accuracy through algorithmic computation.
Solution Approach 2:
The system enables self-service automation where the computer automatically performs placement position calculation and verification without human intervention. The automated system processes environmental constraints and object placement requirements independently, generating valid placement positions through self-contained computational logic rather than requiring manual specification.
2Reliability
If manual placement verification is performed, then constraint compliance is ensured, but time consumption increases
Solution Approach 1:
The patent substitutes manual verification processes with automated computational verification. The system automatically checks placement positions against environmental constraints and object requirements, ensuring constraint compliance through algorithmic validation rather than manual inspection, thereby maintaining reliability while dramatically reducing verification time.
3Manufacturing precision
If specialized skills are required for object placement, then placement precision is maintained, but ease of operation is reduced
Solution Approach 1:
The patent replaces the need for specialized manual skills with an automated computer-based placement system. The system automatically handles complex calculations of valid placement positions based on environmental constraints and object requirements, eliminating the need for users to possess specialized placement knowledge while maintaining high placement precision through algorithmic computation.
Solution Approach 2:
The system performs self-contained computational analysis to determine valid placement positions, automatically processing environmental data and constraint conditions without requiring user expertise. The automated generation of placement positions based on processed constraints makes the system accessible to users regardless of their specialized knowledge level.
4Manufacturing precision
If manual scaling for large numbers of objects is attempted, then individual placement quality is maintained, but productivity decreases
Solution Approach 1:
The patent replaces manual scaling processes with automated batch processing capability. The system can simultaneously calculate valid placement positions for large numbers of objects by processing environmental constraints and object requirements through computational algorithms, maintaining individual placement quality while achieving scalable productivity that manual methods cannot provide.
Data Source
AI summary
Systems and methods are disclosed for permitting the use of a natural language expression to specify object (or asset) locations in a virtual three-dimensional (3D) environment. By rapidly identifying and solving constraints for 3D object placement and orientation, consumers of synthetics services may more efficiently generate experiments for use in development of artificial intelligence (AI) algorithms and sensor platforms. Parsing descriptive location specifications, sampling the volumetric space, and solving pose constraints for location and orientation, can produce large numbers of designated coordinates for object locations in virtual environments with reduced demands on user involvement. Converting from location designations that are natural to humans, such as “standing on the floor one meter from a wall, facing the center of the room” to a six-dimensional (6D) pose specification (including 3-D location and orientation) can alleviate the need for a manual drag/drop/reorient procedure for placement of objects in a synthetic environment.