Signed Distance Function Rendering for Complex Mesh Geometry
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Solution Overview
Problem
Rendering complex and detailed geometry represented by a mesh is computationally and memory-intensive due to the large number of triangles involved, necessitating a more efficient method to reduce these costs.
Innovation Solution
A data processing apparatus and method that utilizes a machine learning model, such as a convolutional neural network, to generate a representative signed distance function (SDF) from a triangular mesh, which is then used for rendering, allowing for a fraction of the computational and memory cost compared to traditional methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mesh rendering methods are used to represent complex geometry, then visual fidelity is maintained, but computational cost and memory requirements become extremely high
Solution Approach 1:
The patent transforms the geometric representation from discrete mesh parameters (vertices, triangles) to continuous field parameters (signed distance values on a grid). This parameter change allows the object to be represented by distance values rather than explicit triangle definitions, reducing computational complexity while preserving visual accuracy through the continuous nature of the field representation
Solution Approach 2:
The patent replaces the traditional geometric rendering mechanism (triangle-based rasterization) with a field-based mechanism (signed distance field sampling and rendering). This substitution enables more efficient computation by using distance field mathematics instead of complex geometric intersection calculations, achieving both speed improvements and memory reduction
2Manufacturing precision
If a large number of triangles are used to represent detailed geometry, then object detail and accuracy are preserved, but memory requirements increase significantly
Solution Approach 1:
The patent changes the representation parameters from discrete triangle data (requiring storage of vertex positions, normals, and connectivity for millions of triangles) to a continuous signed distance field defined on a regular grid. This parameter transformation maintains geometric accuracy through the continuous distance function while dramatically reducing memory requirements by eliminating the need to store explicit triangle topology and vertex data
Data Source
AI summary
A data processing apparatus comprising circuitry configured to: obtain data representing a boundary representing a virtual object in a virtual space; determine a signed distance field of the boundary in the virtual space; determine, by comparing a value of the signed distance field at each of a plurality of points of the virtual space with an output of one or more candidate signed distance functions at each of the plurality of points of the virtual space, a representative signed distance function representing the signed distance field; and output data representing the representative signed distance function.