3D Mesh Uniformity via Region Diffusion Sampling
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Solution Overview
Problem
Manipulating digital representations of 3D objects with varying triangle sizes is challenging, particularly when trying to transform or display objects with lower triangle density, as existing methods struggle to uniformly process and render such meshes efficiently.
Innovation Solution
A region diffusion engine identifies points on the surface of a 3D object and grows regions from these points, creating a pattern of points that can be used to generate a new 3D mesh with more uniform triangles, allowing for efficient triangulation and improved processing times regardless of sampling density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a 3D mesh with varying triangle sizes is used to represent a 3D object, then the representation can capture detailed geometric features, but the manipulation and processing becomes difficult and inefficient
Solution Approach 1:
The patent segments the 3D object surface into multiple regions using a region diffusion approach. Each region is further divided into multiple sampling points, allowing the complex mesh to be processed in manageable segments. This segmentation enables uniform sampling density across different regions while preserving the overall geometric detail.
Solution Approach 2:
The patent applies local quality by creating regions with different sampling densities based on the local geometric characteristics of the 3D object. Areas with complex geometry receive higher sampling density to capture details, while simpler areas use lower density, optimizing both precision and processing efficiency.
2Productivity
If existing methods process 3D meshes with lower triangle density, then processing time increases, but uniform processing across different densities is not achieved
Solution Approach 1:
The patent performs preliminary region diffusion and sampling point identification before the actual processing. By pre-dividing the mesh into regions and identifying sampling points in advance, the subsequent processing operations can proceed more efficiently with consistent time complexity regardless of the original mesh density.
Solution Approach 2:
The patent changes the processing parameters by using a fixed number of sampling points per region rather than relying on the original triangle density. This parameter change ensures that processing time remains constant across different mesh densities, as the number of operations is determined by the region structure rather than the triangle count.
3Device complexity
If uniform sampling density is applied across the entire 3D object surface, then processing becomes simpler, but detailed features in complex areas are lost
Solution Approach 1:
The patent implements local quality by varying the sampling density according to the local geometric complexity of each region. Regions with complex curvature or detailed features receive higher sampling density, while smoother regions use lower density, thus preserving details where needed without unnecessarily increasing processing complexity everywhere.
Solution Approach 2:
The patent introduces dynamics by allowing the sampling density to vary dynamically based on the region's geometric characteristics. The region diffusion process adaptively determines sampling points based on local curvature and geometry, creating a dynamic sampling strategy that responds to the object's features rather than applying a static uniform density.
Data Source
AI summary
In some examples, a system receives a first representation of a three-dimensional (3D) object that has a surface, and identifies points on the surface, where identifying a first point includes defining a region extending in a plurality of different directions from a second point across the surface until a criterion is satisfied, and where a location of the first point is based on a perimeter of the region. The system generates, for image output, a second representation based on the identified points.