Mesh Model Detail Enhancement via Point Cloud Segmentation
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Solution Overview
Problem
Existing graphics hardware and software are optimized for mesh-based graphics, making it computationally expensive to work with point clouds, which lack compatibility with optimized hardware and software.
Innovation Solution
A graphics system aligns point cloud and mesh-based representations of a 3D object, detects regions-of-interest with greater positional variety, and reconstructs these regions by defining additional triangles or meshes to incorporate the depth and detail of point clouds into mesh models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point clouds are used to represent 3D objects, then graphical fidelity, depth, and detail are improved, but computational cost and resource consumption increase significantly
Solution Approach 1:
The patent segments the point cloud data by identifying multiple regions of interest within the 3D space, each requiring different levels of mesh detail. This allows selective processing where only certain regions are reconstructed with high fidelity using point cloud data, while other regions use standard mesh representation, thereby reducing overall computational cost while maintaining graphical fidelity in critical areas.
Solution Approach 2:
The patent applies local quality by differentiating mesh reconstruction strategies across different spatial regions. Regions of interest receive detailed mesh reconstruction from point cloud data to achieve high graphical fidelity, while non-critical regions use coarser mesh representation. This selective approach optimizes the balance between detail quality and computational resource consumption.
2Shape
If point clouds are used to represent 3D objects, then depth and dimensionality are improved, but compatibility with optimized graphics hardware and software deteriorates
Solution Approach 1:
The patent introduces a mesh reconstruction process as an intermediary between point cloud data and graphics hardware rendering. The point cloud data is processed to generate mesh representations that serve as a compatible intermediate format, enabling the depth and dimensionality information from point clouds to be conveyed to optimized graphics hardware through the mesh format that the hardware is designed to handle efficiently.
3Productivity
If mesh-based representation is used, then processing efficiency is improved, but graphical fidelity and detail are reduced
Solution Approach 1:
The patent segments the 3D space into multiple regions of interest, allowing different processing strategies to be applied to different regions. Critical regions undergo detailed mesh reconstruction from point cloud data to achieve high graphical fidelity, while non-critical regions maintain standard mesh representation for processing efficiency. This selective segmentation resolves the contradiction by applying high-fidelity processing only where necessary.
Solution Approach 2:
The patent implements local quality by varying the level of mesh detail across different spatial regions based on their importance. Regions of interest receive enhanced mesh reconstruction with higher polygon density and greater geometric detail to achieve superior graphical fidelity, while other regions use coarser mesh representation to maintain overall processing efficiency.
Data Source
AI summary
Disclosed is a graphics system and associated methodologies for selectively increasing the level-of-detail at specific parts of a mesh model based on a point cloud that provides a higher detailed representation of the same or similar three-dimensional (“3D”) object. The graphics system receives the mesh model and the point cloud of the 3D object. The graphics system determines a region-of-interest of the 3D object based in part on differences amongst points that represent part or all of the region-of-interest. The graphics system reconstructs the region-of-interest in the mesh model and generates a modified mesh model by modifying a first set of meshes representing the region-of-interest in the mesh model to a second set of meshes based on the positional elements of the point cloud points. The second set of meshes has more meshes and represents the region-of-interest at a higher level-of-detail than the first set of meshes.