Bounding Volume Trajectory for Image Rendering Sample Efficiency
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Solution Overview
Problem
Conventional methods for improving sample test efficiency in image rendering, such as motion blur and depth of field rendering, suffer from low sample test efficiency due to the need to test a large number of sample points, especially when primitives move quickly across the screen, leading to decreased image quality and increased computational costs.
Innovation Solution
Constructing a bounding volume characterized by a non-screen space dimension defined as a function of the primitive's trajectory, which allows for the exclusion of sample points not overlapped by the bounding volume from testing, thereby reducing the number of samples tested for rendering a screen space region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sample testing algorithms test all sample points in covered pixels to ensure accurate color computation, then image quality is maintained, but sample test efficiency deteriorates drastically (can be as low as 1%) when primitives move quickly across the screen
Solution Approach 1:
The invention segments the set of sample points into two categories: those that require testing and those that can be excluded. By dividing the pixel area into regions that may contain the moving primitive and regions that definitely do not, the algorithm selectively tests only relevant samples, maintaining accuracy where needed while improving efficiency by excluding irrelevant samples from testing.
Solution Approach 2:
The invention performs preliminary computation of the primitive's trajectory and screen-space bounds before conducting sample tests. By pre-calculating the path the primitive will take across pixels during the exposure period, the algorithm can determine in advance which sample points are worth testing, avoiding wasted tests on samples that the primitive cannot possibly overlap.
2Measurement precision
If the number of samples per pixel is increased to improve image quality in motion blur and depth of field rendering, then rendering accuracy improves, but computational cost increases significantly
Solution Approach 1:
The invention applies partial action by testing only a subset of sample points rather than all samples. By using bounding volumes to identify and exclude samples that cannot be overlapped by the primitive, the algorithm performs fewer tests (partial action) while still maintaining rendering accuracy for the relevant samples, thereby reducing computational cost without sacrificing image quality.
3Measurement precision
If time-continuous triangles are used to cover the entire exposure time to support motion blur, then a unique time value can be set to each sample improving image quality, but sample test efficiency deteriorates because the triangle can cover a relatively large region on the screen
Solution Approach 1:
The invention extends the problem from 2D screen space to 3D by incorporating the time dimension. By computing the primitive's trajectory through screen space over time and creating a 3D bounding volume that encompasses this temporal path, the algorithm can efficiently determine which samples are relevant by checking their spatial and temporal overlap with the bounding volume, thus maintaining image quality while improving sample test efficiency.
Data Source
AI summary
A method for reducing the number of samples tested for rendering a screen space region of an image includes constructing a trajectory of a primitive extending within an image which is to be rendered. A bounding volume is constructed for a screen space region of the image, the bounding volume characterized as having a bound in a non-screen space dimension which is defined as a function of the primitive's trajectory. The bounding volume is further characterized as overlapping a portion of the screen space region which is to be rendered. One or more sample points which are located within the screen space region, and which are not overlapped by the bounding volume are excluded from testing.