3D Shape Segmentation Using Scene Graph Hierarchies
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Solution Overview
Problem
Existing solutions for segmenting and labeling 3D models are inefficient due to the need for extensive manual effort in creating pre-labeled training data and lack of consistent labeling schemes, leading to semantically meaningless labels when automated algorithms are used.
Innovation Solution
A computing system that generates a segmentation model using user-provided labels and scene graphs to hierarchically segment 3D shapes by transforming feature vectors into a common feature space, identifying component-shape groups, and training a point classifier to assign meaningful labels to input 3D shapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If segmentation algorithms are trained in a highly supervised manner using manually generated pre-labeled training data, then the segmentation accuracy and semantic meaning of labels are improved, but the time and effort required for data preparation increases significantly
Solution Approach 1:
The system performs preliminary action by automatically generating synthetic labeled training data through rendering engine simulations before actual segmentation tasks. This pre-computed training data includes synthetic point clouds with ground truth labels, eliminating the need for manual annotation while providing high-quality training examples for the neural network.
Solution Approach 2:
The system implements self-service by enabling algorithms to automatically generate their own training data through the rendering engine. The system autonomously creates synthetic 3D models, renders them with various conditions, and generates corresponding labels without external human intervention, allowing the segmentation algorithm to train on self-generated data.
2Productivity
If automated segmentation algorithms are used without pre-labeled training data, then the productivity and ease of operation are improved, but the labels assigned to component shapes become semantically meaningless
Solution Approach 1:
The system introduces an intermediary rendering engine that acts as a bridge between automated segmentation and semantic labeling. This intermediary component generates synthetic training data with ground truth labels, allowing the automated algorithm to learn semantic meanings without requiring manual annotation of actual target data, thus preserving both speed and semantic information.
Solution Approach 2:
The system changes the parameter space by transforming real-world 3D models into synthetic representations through the rendering engine. By manipulating parameters such as lighting, material properties, and camera angles in the synthetic environment, the system generates diverse training examples that maintain semantic accuracy while enabling automated processing.
3Ease of manufacture
If publicly available 3D model data with user-provided labels is used directly for training, then the ease of obtaining training data is improved, but the inconsistency in labeling schemes across different users reduces reliability
Solution Approach 1:
The system applies homogeneity by standardizing all labels through a unified rendering engine framework. Even though input 3D models come from diverse sources with different labeling conventions, the system processes them through a consistent synthetic rendering pipeline that enforces uniform labeling standards, ensuring homogeneous and reliable training data across all examples.
Solution Approach 2:
The rendering engine serves multiple functions: it acts as a data generator, label annotator, and standardization mechanism simultaneously. This universal tool processes diverse input formats and labeling schemes from different users, transforming them into a consistent training format that maintains ease of data acquisition while ensuring labeling reliability.
Data Source
AI summary
This disclosure involves generating and outputting a segmentation model using 3D models having user-provided labels and scene graphs. For example, a system uses a neural network learned from the user-provided labels to transform feature vectors, which represent component shapes of the 3D models, into transformed feature vectors identifying points in a feature space. The system identifies component-shape groups from clusters of the points in the feature space. The system determines, from the scene graphs, parent-child relationships for the component-shape groups. The system generates a segmentation hierarchy with nodes corresponding to the component-shape groups and links corresponding to the parent-child relationships. The system trains a point classifier to assign feature points, which are sampled from an input 3D shape, to nodes of the segmentation hierarchy, and thereby segment the input 3D shape into component shapes.