3D Scene Partitioning for Real-Time Simulation
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Solution Overview
Problem
Existing methods for partitioning 3D scenes into zones for real-time processing by computing resources are inefficient, requiring manual selection and being time-consuming, especially when aiming for the optimal number of zones for real-time simulation.
Innovation Solution
A computer-implemented method that partitions a 3D scene into zones by computing a density map of computing costs, defining constraints on zone shapes, discretizing the scene into cells, and aggregating cells into zones using a combinatorial optimization algorithm to ensure real-time simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual selection method is used for partitioning 3D scene into zones, then partitioning can be performed, but it is time-consuming and inefficient
Solution Approach 1:
The system performs automatic partitioning of the 3D scene into zones using computational algorithms that analyze the scene independently, eliminating the need for manual user selection. The computer program automatically identifies optimal zone boundaries and configurations based on scene characteristics, achieving self-service partitioning that is both efficient and adaptive to different scene types.
2Reliability
If the number of zones is increased to improve real-time simulation capability, then simulation accuracy improves, but computational complexity increases
Solution Approach 1:
The system dynamically adjusts the number and configuration of zones based on computational parameters and scene characteristics. By changing parameters such as zone size, shape, and distribution density, the system optimizes the balance between simulation accuracy and computational complexity, creating an adaptive partitioning strategy that responds to real-time requirements.
Solution Approach 2:
The partitioning system is designed to be dynamic rather than static, allowing zone configurations to adapt based on scene complexity, object density, and computational resource availability. This dynamic approach enables the system to optimize the number of zones for each specific scenario, avoiding unnecessary complexity while maintaining real-time simulation capability.
3Productivity
If optimal number of zones is targeted for real-time simulation, then simulation performance improves, but the selection process becomes extremely time-consuming
Solution Approach 1:
The system performs preliminary analysis of the 3D scene to pre-determine optimal zone configurations before actual simulation begins. By conducting upfront computational analysis of scene characteristics, object distributions, and potential interaction zones, the system prepares optimal partitioning schemes in advance, eliminating the need for time-consuming trial-and-error selection during simulation operations.
Solution Approach 2:
The system incorporates feedback mechanisms that evaluate simulation performance and automatically adjust zone configurations. By monitoring simulation results and computational efficiency, the system learns from each partitioning attempt and refines its zone selection algorithm, progressively improving performance while reducing the time required to identify optimal configurations.
Data Source
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AI summary
The invention notably relates to a computer-implemented method for partitioning a 3D scene into a plurality of zones, each zone representing an area or a volume of the 3D scene and being processed by a computing resource. The method comprises providing a 3D scene comprising one or more objects, each object generating a computing resource cost; computing a first map that represents a density of computing costs of the provided 3D scene; defining a second map that represents constraints on the shapes of zones that will be obtained as a result of a partitioning of the 3D scene; discretizing the provided 3D scene into cells by computing a space quantization of the 3D scene free of dynamic objects, a dynamic object being an object that can potentially move in the 3D scene; computing, for each cell, a computing cost from the first map of the 3D scene; aggregating the cells into one or more zones in accordance with the second map, each zone having a computing cost that is the sum of the computing costs of the cells belonging to the zone, the computing cost of the zone allowing a real-time simulation of the zone on a computing resource.