3D Object Positioning via Rule-Based Scene Matching
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Solution Overview
Problem
Existing solutions for automatically positioning 3D objects in 3D scenes are limited by their reliance on neural networks, which can be inefficient and difficult to integrate, and they often fail to accurately predict positions when constraints are inconsistent or unfulfillable.
Innovation Solution
A computer-implemented method that uses a dataset of 3D scenes to identify consistent positioning patterns for 3D objects, allowing for the automatic placement of input 3D objects in a 3D scene by matching them with equivalent objects from the dataset based on classification and size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If neural networks are used to predict object position in 3D scenes, then positioning can be automated, but integration complexity and computational efficiency deteriorate
Solution Approach 1:
The patent replaces neural network-based prediction systems with a rule-based positioning system that uses spatial relationships and constraints. Instead of using complex deep learning models, the invention employs deterministic rules for calculating object positions based on scene geometry and object properties, thereby reducing integration complexity while maintaining automation.
Solution Approach 2:
The patent extracts and removes the neural network component from the positioning system, isolating the core functionality to a simplified rule-based engine. This extraction eliminates the need for complex model training and deployment infrastructure, reducing overall system complexity while preserving the automated positioning capability.
2Extent of automation
If neural networks are used to predict object position, then positioning can be automated, but computational efficiency deteriorates
Solution Approach 1:
The patent substitutes neural network computations with efficient rule-based calculations that leverage existing scene data and object properties. The positioning is achieved through direct mathematical computations based on spatial constraints and geometric relationships, which are significantly faster and more computationally efficient than training and inference of neural networks.
3Extent of automation
If neural networks predict object position, then positioning can be performed, but accuracy deteriorates when constraints are inconsistent
Solution Approach 1:
The patent implements a feedback mechanism that detects and responds to inconsistent constraints by attempting to resolve conflicts through rule-based reasoning. When constraints are found to be contradictory, the system identifies the conflict and adjusts the positioning approach accordingly, either by relaxing constraints or by selecting alternative valid positions, thereby maintaining high reliability in the positioning results.
Solution Approach 2:
The patent performs preliminary validation of constraints before executing the positioning operation. By checking for consistency and feasibility of constraints in advance, the system prevents attempts to satisfy impossible requirements, ensuring that only valid positioning solutions are generated and thereby maintaining high accuracy and reliability.
4Measurement precision
If detailed object classification is used for positioning, then positioning accuracy improves, but system complexity increases
Solution Approach 1:
The patent applies local quality by tailoring the level of object classification and positioning precision to the specific requirements of each object type and scene context. Rather than using uniformly detailed classification across all objects, the system adjusts the granularity of classification and positioning rules based on the object's functional requirements and spatial relationships, thereby achieving high accuracy where needed while reducing complexity where sufficient precision can be obtained with simpler rules.
Data Source
AI summary
A computer-implemented method for automatically positioning an input 3D object representing a real object in an input 3D scene representing a room. The method includes obtaining a dataset having information about objects of a plurality of rooms. The method includes executing computer program instructions that cause attempting to identify first, second and/or third pairs. The method includes outputting one or more pairs among the set consisting of each identified pair and the counts of the one or more identified pairs. The method includes, for each outputted pair, determining a respective position of the input 3D object in the input 3D scene. The method includes positioning the input 3D object according to the respective position determined for one of the one or more outputted pairs. The method improves the positioning of an input 3D object in an input 3D scene.