GPU-Based Kd-Tree Construction for Dynamic Ray Tracing
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Solution Overview
Problem
Existing CPU-based algorithms for kd-tree construction in graphics applications are computationally slow, making them unsuitable for dynamically rendering frequently changing scenes, especially in ray tracing which requires fast and efficient data structures.
Innovation Solution
A GPU-based ray tracer builds a kd-tree for each frame of a dynamic scene by differentiating large nodes from small nodes, splitting large nodes using empty space and spatial median splitting, and splitting small nodes based on computed costs, leveraging parallel processing and optimized memory management to enhance performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CPU-based algorithms are used for kd-tree construction, then manufacturing precision is maintained, but productivity is significantly reduced
Solution Approach 1:
The patent replaces CPU-based sequential computation with GPU-based parallel computation for kd-tree construction. The graphics hardware utilizes thousands of cores to perform parallel operations on geometric primitives, substituting the traditional mechanical CPU computation model with a parallel graphics processing model that achieves significantly higher throughput for rendering applications.
Solution Approach 2:
The patent segments the kd-tree construction process into distinct phases: building the tree structure, differentiating large and small nodes, and applying different splitting strategies to each node type. This segmentation allows optimized handling of different node categories, with large nodes using empty space splitting and small nodes using cost-based splitting, thereby improving overall construction efficiency.
2Manufacturing precision
If uniform splitting strategy is applied to all nodes, then device complexity is reduced, but manufacturing precision deteriorates
Solution Approach 1:
The patent applies local quality by differentiating between large nodes and small nodes and applying different splitting strategies to each. Large nodes undergo empty space splitting to efficiently eliminate voids, while small nodes use cost-based splitting for precise optimization. This localized approach ensures each node type receives the most appropriate treatment for its specific characteristics, improving overall tree quality.
Solution Approach 2:
The patent changes the splitting parameter strategy based on node size. For large nodes, the splitting criterion focuses on empty space elimination, while for small nodes, the criterion shifts to computed costs. This parameter adaptation allows the algorithm to optimize for different scales, maintaining high construction quality across varying node sizes without requiring a single complex universal strategy.
Data Source
AI summary
Described is a technology by which a ray tracer incorporates a GPU-based kd-tree builder for rendering arbitrary dynamic scenes. For each frame, the ray tracer builds a kd-tree for the scene geometry. The ray tracer spawns and traces eye rays, reflective and refractive rays, and shadow rays. For each ray to be traced, the ray tracer walks through the kd-tree until it reaches leaf nodes and associated triangles. When a ray passes through both sides of a splitting plane, the “far” sub-tree is pushed into the stack and the “near” sub-tree is traversed first.