3D Model Spherical Expression via Multi-Stage Deformation Reconstruction
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Solution Overview
Problem
Current methods for learning and extracting general-purpose three-dimensional shape expressions in three-dimensional computer graphics are limited by the need for extensive manual annotations and are task-driven, making them inefficient and less applicable across various applications.
Innovation Solution
A three-dimensional model's spherical expression method is proposed, which involves processing an input model into a surface dense point cloud, performing multi-resolution point cloud abstraction, and using a coarse-to-fine auto-encoding framework for deformation reconstruction, allowing for unsupervised learning and encoding of multi-scale features to describe shape attributes and characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If task-driven methods are used to learn and extract three-dimensional shape expressions, then the description ability of feature coding is improved, but the general applicability of learned features deteriorates and heavy manual annotation is required
Solution Approach 1:
The patent segments the three-dimensional shape expression learning process into multiple stages with different abstraction levels. Each stage extracts features at a specific level of detail, allowing the system to capture both coarse-grained structural information and fine-grained geometric details. This multi-stage segmentation enables the learned features to be applied across different tasks and domains without requiring task-specific annotations.
Solution Approach 2:
The patent creates a universal three-dimensional shape expression framework that can be applied to multiple tasks including category recognition, semantic segmentation, and shape reconstruction. By learning general-purpose shape expressions that capture essential geometric characteristics at multiple scales, the system achieves versatility across different applications without requiring task-specific training data or manual annotations for each task.
2Reliability
If task-driven learning methods are used, then specific task performance is improved, but learning efficiency deteriorates due to extensive manual annotation requirements
Solution Approach 1:
The patent implements a self-supervised learning approach where the system automatically generates training signals from the three-dimensional shape data itself, without requiring manual annotations. The multi-stage deformation reconstruction process inherently provides supervision by comparing reconstructed shapes with ground truth, enabling the system to learn general-purpose shape expressions efficiently at scale.
3Productivity
If multi-resolution point cloud abstraction is performed, then computational efficiency is improved, but feature extraction complexity increases
Solution Approach 1:
The patent segments the feature extraction process into multiple resolution levels, where each level processes point clouds at a different scale. This segmentation allows computational resources to be distributed across levels, processing fewer points at coarser levels and more points at finer levels, thereby improving overall computational efficiency while systematically managing the complexity of feature extraction at each stage.
Data Source
AI summary
The embodiments of the present disclosure disclose a three-dimensional model's spherical expression calculation method. A specific implementation of the method includes: processing an input three-dimensional model into a dense point cloud model; inputting the dense point cloud model to a multi-level feature extraction module, extracting high-dimensional feature vectors of different abstract levels; inputting the high-dimensional feature vectors, template ball point cloud and abstract preprocessed point cloud model to a point cloud deformation reconstruction module to obtain a deformed reconstruction point cloud model; extracting multi-stage deformation reconstruction process information, combining the multi-stage deformation reconstruction process information with the template ball point cloud to together form complete information describing the three-dimensional model; obtaining a density correspondence from the three-dimensional model to the template ball point cloud and a density correspondence between different three-dimensional models. This implementation does not require time-consuming and labor-intensive manual annotations, improving the efficiency of characterization learning.