3D Mesh Decimation via Depth Map Reconstruction
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Solution Overview
Problem
Traditional mesh decimation technologies face challenges in reducing polygon counts of complex 3D models without creating holes or gaps in critical areas, especially when dealing with 'polygon soup' data where connections between triangles are undefined, leading to undesirable rendering issues.
Innovation Solution
The approach involves capturing depth map data from multiple perspectives to identify visible components and exclude hidden ones, generating point cloud data, and applying surface reconstruction to produce mesh data with a reduced polygon count while maintaining salient features, using techniques like raster-based mesh decimation and mesh decimation algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional mesh decimation algorithms are used to reduce polygon counts, then rendering performance improves, but holes and gaps appear in critical areas of the model
Solution Approach 1:
The patent performs preliminary actions by capturing depth map data from multiple perspectives before decimation to identify visible surfaces and critical areas. This advance preparation allows the algorithm to know which areas must be preserved, preventing holes and gaps from forming in critical regions during the subsequent polygon reduction process.
Solution Approach 2:
The system uses feedback by comparing depth map data from multiple viewpoints to identify which polygon components are actually visible from various perspectives. This feedback mechanism allows the algorithm to selectively preserve only the necessary polygons that contribute to visible surfaces, maintaining model accuracy while removing redundant hidden components.
2Speed
If random polygon components are removed to reduce polygon count, then processing speed improves, but salient features such as front face triangles are accidentally removed
Solution Approach 1:
The patent employs feedback by using depth map data from multiple perspectives to determine which polygon components represent salient features. This feedback allows the algorithm to distinguish between removable background polygons and critical feature-defining triangles, ensuring that salient features like front face triangles are preserved while removing only non-essential components.
Solution Approach 2:
The system changes parameters by evaluating polygon components based on their visibility across multiple perspectives rather than treating all polygons equally. By changing the selection criterion from random to visibility-based, the algorithm can rapidly remove inappropriate polygons while preserving those that define salient features, achieving both speed and accuracy.
3Reliability
If hidden internal components are retained during mesh decimation, then model completeness is maintained, but rendering efficiency decreases due to unnecessary polygon processing
Solution Approach 1:
The patent applies the extraction principle by separating visible polygon components from hidden internal components based on depth map analysis. The algorithm extracts and removes hidden components that do not contribute to the visible model appearance, thereby improving rendering efficiency without compromising the completeness of the visible model structure.
Solution Approach 2:
The system creates copies of the model from multiple perspectives using depth map data to determine visibility. By comparing these perspective copies, the algorithm identifies which components are consistently hidden across all views and can be safely removed, maintaining visible model completeness while eliminating redundant hidden polygons for improved rendering efficiency.
4Device complexity
If polygon soup data structure is used, then data storage is simplified, but traditional decimation algorithms cannot effectively reduce polygons without creating holes
Solution Approach 1:
The patent uses feedback by processing polygon soup data through multiple depth map perspectives to gain spatial context. This feedback transforms the unordered data into a structured understanding of visible surfaces, allowing the algorithm to prevent holes while maintaining the simplicity of the original polygon soup data structure for storage.
Solution Approach 2:
The system applies dimensionality change by adding the temporal dimension of multiple perspectives to the spatial analysis of polygon soup data. By evaluating polygons across multiple viewpoint dimensions, the algorithm can identify and preserve critical connections that prevent holes, while still storing the original simple polygon soup structure without requiring complex preprocessing reorganization.
Data Source
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AI summary
Concepts and technologies are described herein for providing raster-based mesh decimation. Generally described, input data defining a model is processed to render the model as a depth map from a multitude of perspectives. By capturing depth map data from a multitude of perspectives, components of the model that are visible from the multitude of perspectives are captured in the depth map data and components that are blocked by the visible components are not captured in the depth map data. Point cloud data is generated by merging the depth map data captured from the multitude of perspectives. Output mesh data is generated by applying a surface reconstruction to the point cloud data. One or more mesh decimation algorithms may be applied to the mesh data. Image data may also be captured from the input data and applied to the output mesh data.