Description
Large repositories of 3D shapes are invaluable for data-driven analysis and modeling, especially when annotated with semantic information like salient regions and functional parts. This paper proposes a novel active learning method designed to enrich massive geometric datasets with accurate semantic region annotations. The core objective is to correctly demarcate corresponding regions in a shape collection with minimal manual intervention, given a user-specified region label.
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Our active framework operates in a cyclical process: manual annotation of initial regions, automatic propagation of these annotations across the remaining shapes, manual verification of both human and automatic annotations, and learning from these verification results to refine the automatic propagation algorithm. A unified utility function is employed to explicitly model the time cost of human input at all stages. This allows for the joint optimization of which models to annotate and which to verify, based on their predicted impact on human efficiency.
The method demonstrates that incorporating verification of all produced labelings within this unified objective enhances both the accuracy and efficiency of the active learning procedure. Human labels are automatically propagated across a dynamic shape network using a conditional random field (CRF) framework. This propagation leverages global shape-to-shape similarities, local feature similarities, and point-to-point correspondences, achieving higher accuracy than existing alternatives by combining these diverse cues.
Validation on existing benchmarks shows the framework to be significantly more efficient in utilizing human input compared to previous techniques. Further validation of its efficiency and robustness is demonstrated by annotating a massive shape dataset, labeling over 93,000 shape parts across multiple model classes, and providing a labeled part collection that is more than an order of magnitude larger than existing ones. The pipeline involves selecting an annotation set, obtaining human labels via a UI, automatically propagating these labels, querying users to verify confident propagations, and using these verifications to improve the propagation technique.
- CategoryMachine Learning Platforms
- DeploymentCloud
- PlatformsWeb
Scalable Active Framework for Region Annotation Highlights
Active learning for semantic region annotation in 3D shapes
Automatic propagation of annotations across shape collections
Unified utility function for optimizing annotation and verification tasks
Conditional Random Field (CRF) framework for label propagation
Integration of global shape similarities, local features, and point correspondences
Minimizes manual annotation effort through efficient verification
Demonstrated efficiency and accuracy improvements over existing methods
Scalable to massive 3D shape datasets
Provides detailed per-point labeling
Supports multiple shape categories and model classes
Facilitates creation of large, labeled part collections
Getting Started with Scalable Active Framework for Region Annotation
Access model: Obtain the framework code or pre-release data.
Integrate via API: Utilize the provided propagation and annotation tools.
Annotate regions: Manually label initial regions in selected 3D shapes.
Propagate labels: Automatically extend annotations to the rest of the dataset.
Verify annotations: Review and correct automatically propagated labels.
Learn and refine: Use verification results to improve the propagation algorithm.
Analyze results: Leverage the enriched dataset for data-driven analysis.
Scalable Active Framework for Region Annotation's Use Cases
- 3D Model Annotation
- Active Learning for Geometry
- Dataset Enrichment
- Shape Analysis
- Computer Graphics Research
- Robotics and Perception
