Point Cloud Generation from Mesh via Intersection Sampling
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Solution Overview
Problem
Existing methods for generating point clouds from meshes result in high throughput and large data processing loads due to dense sampling and redundant processing, leading to increased processing time and resource usage.
Innovation Solution
An image processing apparatus and method that generate point cloud data by positioning points at intersection points between a mesh surface and vectors with specified resolution coordinates, allowing for a single-step processing of voxel data equivalent to input resolution, thereby reducing processing loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If points are densely sampled on mesh surfaces to generate high-density point cloud, then point cloud quality is improved, but processing load increases
Solution Approach 1:
The patent segments the mesh processing into two distinct stages: (1) generating a low-density point cloud by sampling only at vertex positions, and (2) subsequently voxelizing this sparse point cloud. This segmentation avoids the need to densely sample the entire mesh surface, thereby reducing processing load while maintaining sufficient point cloud quality for the intended application.
Solution Approach 2:
Instead of performing complete dense sampling across the entire mesh surface, the patent applies partial action by sampling only at vertex positions (a subset of all possible surface points). This partial sampling approach provides sufficient geometric representation for voxelization without the excessive processing burden of full surface sampling.
2Loss of information
If high-density point cloud is generated through dense sampling, then data completeness is improved, but processing time increases
Solution Approach 1:
The patent divides the processing into sequential stages where a sparse point cloud is first generated from mesh vertices, then voxelized in a second stage. This segmentation eliminates the time-consuming dense sampling step while preserving essential geometric information through the vertex-based sampling approach.
Solution Approach 2:
The patent performs preliminary sampling at mesh vertices before voxelization, creating a sparse point cloud that contains sufficient geometric information. This preliminary action at vertex positions alone provides the necessary data foundation for accurate voxelization without requiring subsequent dense sampling operations.
3Measurement precision
If dense sampling is performed on mesh surfaces, then point cloud resolution is improved, but resource usage increases
Solution Approach 1:
The patent segments the resolution achievement into two phases: (1) capturing geometric information at vertex positions during sparse sampling, and (2) achieving final resolution through the voxelization process. This segmentation allows the system to use computational resources efficiently by performing low-cost vertex sampling followed by structured voxelization, rather than expensive dense surface sampling.
Solution Approach 2:
The patent replaces the mechanical approach of densely sampling mesh surfaces with an algorithmic approach: sparse vertex-based sampling followed by voxelization. This substitution uses computational geometry algorithms to achieve the desired resolution efficiently, reducing resource usage compared to traditional dense sampling methods.
Data Source
AI summary
There is provided an image processing apparatus and an image processing method that are capable of suppressing an increase in loads when a point cloud is generated from a mesh. Point cloud data is generated by positioning points at intersection points between a surface of a mesh and vectors each including, as a start origin, position coordinates corresponding to a specified resolution. For example, intersection determination is performed between the surface of the mesh and each of the vectors, and in a case where the surface and the vector are determined to intersect each other, the coordinates of the intersection point are calculated. The present disclosure can be applied to an image processing apparatus, electronic equipment, an image processing method, a program, or the like.