Fast T-spline fitting via recursive patch partitioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional T-spline fitting methods are computationally expensive and slow due to the need to fit all data points at once, making it difficult to achieve fast computation, especially for large point clouds with many fine details.
Innovation Solution
A fast T-spline fitting algorithm that recursively partitions a parameterized point cloud into patches, fits a spline surface into each patch, and connects them to form a T-mesh, determining control points efficiently, thereby reducing computational costs and improving data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional T-spline fitting algorithms are used to fit all data points at once, then surface reconstruction quality is maintained, but computational time and processing speed increase significantly
Solution Approach 1:
The patent divides the point cloud data into multiple patches and processes each patch independently using parallel computation. This segmentation allows the system to maintain surface reconstruction quality while significantly reducing computational time by avoiding the need to process all data points sequentially in a single global fitting operation.
Solution Approach 2:
The patent performs multiple refinement iterations on patches only when the fitting error exceeds a threshold, rather than uniformly refining all patches. This partial action approach maintains reconstruction quality in regions that need it while skipping unnecessary computations in already-sufficient regions, thereby reducing overall computational time.
2Manufacturing precision
If mesh refinement and least squares fitting are performed multiple times to achieve high fitting accuracy, then surface reconstruction quality improves, but computational cost and power consumption increase
Solution Approach 1:
The patent implements adaptive refinement where patches are reprocessed only when their fitting error exceeds a predefined threshold. This prevents unnecessary multiple refinement iterations on patches that already meet accuracy requirements, significantly reducing power consumption while maintaining overall fitting accuracy for the complete surface.
Solution Approach 2:
The patent applies different refinement strategies to different patches based on their individual fitting errors. High-error regions receive multiple refinement iterations to achieve high accuracy, while low-error regions require minimal or no refinement. This local quality approach optimizes the balance between fitting accuracy and power consumption by concentrating computational resources only where needed.
3Manufacturing precision
If T-spline control points are fitted with all data points at once using conventional methods, then comprehensive surface coverage is achieved, but processing speed decreases
Solution Approach 1:
The patent segments the global fitting problem into multiple independent patch-level fitting problems that can be solved in parallel. Each patch is fitted to its corresponding subset of data points independently, achieving comprehensive surface coverage through the aggregation of all patches while dramatically improving processing speed through parallel computation architecture.
Solution Approach 2:
The patent transitions from a single-dimensional sequential global fitting approach to a multi-dimensional parallel patch-based approach. By organizing the fitting process across multiple spatial patches that can be processed simultaneously, the system achieves both comprehensive surface coverage and high processing speed by utilizing parallel computational dimensions.
Data Source
AI summary
A first T-spline fitting system includes a three-dimensional (3D) sensor, a processor and a memory storing a program for representing a point cloud of the scene using T-spline representation. The system executes acquiring an image of the scene using the 3D sensor, generating the point cloud from the image, determining a parameterized domain of the point cloud by mapping data points of the point cloud to values of parameters of the parameterized domain, partitioning recursively the parameterized domain into a set of patches, connecting the patches to form a T-mesh defining topology of control points in the parameterized domain, and fitting a T-spline surface into the point cloud according to the T-mesh to determine positions of the control points.