Neural BVH Geometry for Low-Memory Ray Tracing
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Solution Overview
Problem
Conventional ray tracing and path tracing techniques are computationally intensive and memory-consuming, limiting real-time graphics rendering, especially in environments without specialized graphics processing units, and conventional bounding volume hierarchies struggle to represent dynamic scenes efficiently.
Innovation Solution
Implementing a bounding volume hierarchy that uses neural representations at leaf nodes instead of object primitives, trained to encode spatiality data, allowing efficient ray and path tracing by querying neural networks for visibility and object properties without direct primitive evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional bounding volume hierarchies store object primitives at leaf nodes, then intersection tests can be performed, but memory consumption increases significantly
Solution Approach 1:
The patent creates a neural representation that encodes the visual appearance and geometry of objects stored in the BVH leaf nodes. Instead of storing the actual object primitives (which consume large memory), the system stores a compressed neural encoding that can be queried to retrieve object properties and visibility information, thus reducing memory consumption while maintaining functional capability
Solution Approach 2:
The patent extracts the essential geometric and visibility information from the object primitives and encodes it into a neural representation. This extraction process removes redundant data while preserving the critical information needed for ray tracing and visibility determination, thereby reducing memory requirements
2Measurement precision
If conventional ray tracing performs intersection tests with object primitives, then accurate rendering is achieved, but computational intensity increases
Solution Approach 1:
The neural representation creates a compressed model of the scene geometry that can be queried efficiently during ray tracing. Instead of performing computationally intensive intersection tests with multiple object primitives, the system queries the neural network to obtain visibility and object properties directly, significantly reducing computational intensity while maintaining rendering accuracy
Solution Approach 2:
The system pre-computes and stores neural representations of scene geometry during an offline processing phase. This preliminary encoding allows the ray tracer to query pre-processed information during rendering, avoiding the need for real-time intersection tests with complex primitives and reducing computational load during the rendering process
3Quantity of substance
If neural representations are trained to overfit ground truth data, then memory consumption is reduced, but training complexity increases
Solution Approach 1:
The neural representations are trained in advance using ground truth data from the scene geometry during an offline phase. This preliminary training allows the system to benefit from the trained models during actual rendering without incurring the training complexity during runtime, as the neural networks are already optimized to retrieve accurate object properties and visibility information
Data Source
AI summary
Techniques for neural based geometry in bounding volume hierarchies are described for enabling identification of properties of geometric objects of a scene. In an example, a processing device is operable to receive a bounding volume hierarchy that partitions geometric objects of a three-dimensional scene into bounding volumes individually assigned to respective nodes. At least one said node includes a neural representation encoding neural network information representing a respective said geometric object. The processing device is further operable to render the scene using the bounding volume hierarchy by constructing the respective said geometric object using the neural representation. The processing device is further operable to present the rendered scene for display in a user interface.