Machine-Learning Collision Detection With Neural Mesh Approximation
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Solution Overview
Problem
Conventional collision detection techniques using bounding volume hierarchies in virtual environments trade accuracy for generality and are computationally intensive, leading to poor performance and user experience.
Innovation Solution
Implement a trained machine learning model that approximates an object's mesh to detect collisions, replacing traditional acceleration structures like bounding volume hierarchies, using neural networks configured for accuracy and performance requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bounding volume hierarchies are used for collision detection, then computational efficiency is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/bounding-volume-based collision detection systems with a machine learning model that uses neural networks to predict collisions. Instead of using geometric bounding volumes (AABB, OBB, KD-trees) to approximate object shapes, the system trains a neural network on mesh data to directly predict whether collisions will occur, substituting the mechanical bounding volume approach with an intelligent prediction system that achieves both accuracy and efficiency
Solution Approach 2:
The patent changes the fundamental parameters of collision detection by transitioning from geometric parameters (bounding volume dimensions, hierarchy levels) to learned parameters (neural network weights, activation patterns). The system transforms collision detection from a geometric computation problem into a pattern recognition problem, where the neural network learns optimal parameter representations during training that balance accuracy and computational efficiency
2Device complexity
If bounding volume hierarchies are used for collision detection, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent replaces complex geometric data structures (bounding volume hierarchies, spatial partitioning trees) with a machine learning model that processes mesh data directly. The neural network architecture substitutes the need for hierarchical bounding volume constructions, simplifying the overall system while improving precision through learned representations rather than geometric approximations
3Measurement precision
If machine learning models are used for collision detection, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent applies preliminary action by training the machine learning model offline before runtime collision detection. During the training phase, the neural network learns from extensive mesh data and collision scenarios, storing optimized weights and parameters. At runtime, the pre-trained model performs rapid predictions without requiring intensive computation, thus achieving high precision while minimizing real-time energy consumption
Solution Approach 2:
The patent implements dynamics by making the collision detection system adaptive through machine learning. The neural network can be retrained or fine-tuned based on changing environmental conditions, object types, or collision patterns, allowing the system to optimize its performance characteristics dynamically while maintaining energy efficiency through learned patterns rather than exhaustive geometric calculations
Data Source
AI summary
An apparatus includes a processor and a collision detection unit operatively coupled to the processor. The collision detection unit is configured to process, using a machine learning model, one or more parameters associated with a ray cast in virtual environment comprising an object. The machine learning model is configured to approximate a mesh representing the object. The collision detection unit is further configured to determine if the ray collides with the object based on processing the one or more parameters. In response to determining if the ray collides with the object, the collision detection unit is configured to generate collision data associated with the ray and the object.