Point Cloud 3D Printing With Watertight Mesh and Volume Mapping
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Solution Overview
Problem
Existing 3D printing methods struggle to efficiently convert point cloud data into printable files, as they require large amounts of information to define and process meshes, and fail to accurately represent attributes of spaces or volumes inside the mesh.
Innovation Solution
A method that derives a watertight mesh from point cloud data using normal vectors to determine point locations, allowing for the efficient conversion of point clouds into 3D models for printing while maintaining shape, volume, and color information, combining computational geometry with field approaches to improve information content and reduce computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a mesh is used to define points, faces, and colors in point cloud data, then the 3D model can be represented with detailed geometric information, but a very large amount of information needs to be generated, stored and processed
Solution Approach 1:
The patent extracts only the essential geometric information needed for 3D printing from the point cloud data, rather than processing the entire detailed mesh. By identifying and removing redundant data elements, the system maintains manufacturing precision while significantly reducing the volume of information that needs to be stored and processed.
Solution Approach 2:
The patent applies different levels of detail and processing to different regions of the 3D model based on their importance. Critical geometric features receive higher precision treatment while less important areas use simplified representations, optimizing the balance between accuracy and data complexity.
2Shape
If a mesh is used to represent 3D data, then surface geometry can be defined, but attributes of spaces or volumes inside the mesh cannot be defined or represented
Solution Approach 1:
The patent transitions from representing only surface geometry (2D mesh) to incorporating volumetric information (3D space attributes). By adding the dimension of volume and spatial relationships, the system can now represent both surface characteristics and internal space attributes simultaneously, eliminating information loss about internal structures.
3Reliability
If traditional mesh methods are used to convert point cloud data, then a complete surface representation is achieved, but computational resources and processing time are excessive
Solution Approach 1:
The patent performs partial processing of the point cloud data by focusing only on the essential elements needed for accurate 3D printing. Rather than processing the entire dataset with full mesh generation, the system applies selective processing to critical regions, achieving reliable surface representation with significantly reduced computational effort and faster conversion times.
Data Source
AI summary
Methods and systems are provided, which convert points in a cloud into a model for 3D printing in a computationally efficient manner and while maintaining and possibly adjusting shape, volume and color information. Methods include deriving, from the points, a crude watertight mesh with respect to the points, e.g., an alpha shape, determining, using normal vectors associated with the points, locations of the points with respect to the mesh (e.g., as being inside, outside or within the model) and using the derived mesh to define the model with respect to the determined locations of the points. Combining the computational geometry approach with the field approach is synergetic and results in better information content of the resulting model for 3D printing while consuming less computational resources.