Triangle Mesh Generation from Depth Map via K-D Tree Segmentation
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Solution Overview
Problem
Current methods for converting three-dimensional image data represented by a two-dimensional image and a depth map into a polygon mesh and texture format are inefficient, leading to suboptimal results with imperfections such as holes and high computational complexity, which hinders accurate and contiguous triangle mesh generation for 3D graphics processing.
Innovation Solution
The approach involves generating a k-D tree from the depth map, positioning internal vertices within undivided regions, and forming triangles between these vertices to create a contiguous triangle mesh, allowing for efficient and accurate 3D image representation suitable for graphics processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional methods are used to convert depth map to polygon mesh, then conversion can be performed, but the resulting mesh contains holes and imperfections with high computational complexity
Solution Approach 1:
The depth map is divided into multiple triangular regions that form a structured mesh. By segmenting the conversion process into discrete triangular units with shared vertices, the method achieves both high precision (no holes) and computational efficiency (reusable vertex processing).
Solution Approach 2:
Vertices are pre-positioned at specific locations (e.g., corners and centers) of triangular regions before mesh generation. This preliminary vertex placement ensures that the mesh is contiguous without holes and enables efficient batch processing of shared vertices across multiple triangles.
2Productivity
If triangle mesh is generated for 3D graphics processing, then processing efficiency can be improved, but the conversion from depth map to mesh format is computationally intensive
Solution Approach 1:
Adjacent triangular regions share common vertices, allowing the processing of a single vertex to be applied to multiple triangles simultaneously. This merging approach reduces the total number of processing operations needed, improving productivity while maintaining mesh accuracy.
Solution Approach 2:
The method transforms the depth map data into a structured mesh representation by changing parameters such as vertex coordinates, triangle connectivity, and depth values. This parameter transformation enables efficient GPU processing while reducing the computational burden of the conversion process through systematic vertex sharing.
3Reliability
If conventional mesh conversion is used, then format conversion can be achieved, but the result is not contiguous and suitable for high-quality 3D display
Solution Approach 1:
The depth map is segmented into triangular regions with explicitly defined shared vertices and edges. This segmentation ensures that adjacent triangles are properly connected, creating a contiguous mesh structure without holes or gaps, while the systematic approach keeps conversion complexity manageable.
Data Source
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AI summary
An apparatus is arranged to generate a triangle mesh for a three dimensional image. The apparatus includes a depth map source (101) which provides a depth map and a tree generator (105) generates a k-D tree from the depth map. The k-D tree representing a hierarchical arrangement of regions of the depth map satisfying a requirement that a depth variation measure for undivided regions is below a threshold. A triangle mesh generator (107) positions an internal vertex within each region of the k-D tree. The triangle mesh is then generated by forming sides of triangles of the triangle mesh as lines between internal vertices of neighboring regions. The approach may generate an improved triangle mesh that is suitable for many 3D video processing algorithms.