SAMPart3D : Segmenter n'importe quelle partie dans des objets 3D
SAMPart3D: Segment Any Part in 3D Objects
Résumé
Summary
AI-Generated Summary
Paper Overview
This paper introduces a multi-granular segmentation visualization of point clouds and meshes, enhancing the understanding of segmented parts through semantic annotations. It also presents SAMPart3D, a framework for 3D part segmentation without explicit annotations, achieving superior performance in zero-shot scenarios.
Core Contribution
- Introducing a multi-granular segmentation visualization for point clouds and meshes.
- Development of SAMPart3D, a framework for 3D part segmentation without annotations, outperforming existing methods in zero-shot settings.
Research Context
The research addresses the need for improved 3D part segmentation techniques without relying on predefined labels or textual prompts, leveraging vision and language models for knowledge distillation.
Keywords
3D Part Segmentation, Point Clouds, Meshes, Semantic Annotations, SAMPart3D, Zero-shot Segmentation, Vision-Language Models
Background
The research aims to fill the gap in 3D part segmentation by proposing SAMPart3D, a framework that overcomes the limitations of existing annotated datasets and generalizes well to open 3D objects.
Research Gap
Existing methods lack the ability to segment 3D parts without prior annotations, hindering their applicability to diverse and complex objects.
Technical Challenges
Challenges include generalizing to open 3D objects, utilizing unannotated 3D knowledge, and managing semantic ambiguity in 3D part segmentation.
Prior Approaches
Previous methods heavily relied on annotated datasets or textual cues for 3D part segmentation, limiting their adaptability to new objects and scenarios.
Methodology
The methodology involves leveraging large-scale pre-training, fine-tuning on specific samples, and semantic querying without training to achieve effective 3D part segmentation.
Theoretical Foundation
Utilizing DINOv2 for distilling 2D visual features into a 3D base network, and employing vision-language models for semantic labeling.
Technical Architecture
Incorporating FeatUp for enhancing DINOv2 features, performing scale-conditioned clustering for 3D point clouds, and introducing long-range connections for capturing low-level features.
Implementation Details
Utilizing MSE loss for distilling 2D features, specific sample fine-tuning for 2D mask distillation, and HDBSCAN clustering for feature grouping.
Innovation Points
- Introducing SAMPart3D for 3D part segmentation without annotations.
- Utilizing vision-language models for semantic querying in 3D object parts.
Experimental Validation
The experimental validation involves thorough evaluations on the PartObjaverse-Tiny dataset, comparisons with existing methods, and ablation studies to analyze the model components' impact.
Setup
- Pre-training on 200,000 high-quality objects for 7 days using PTv3-object model.
- Fine-tuning with 15,000 mesh surface points and 36 object views for 2D segmentation mask generation.
- Utilization of HDBSCAN for feature clustering and GPT-4o for semantic queries.
Metrics
Evaluation based on segmentation accuracy, generalization to unseen objects, and efficiency in zero-shot scenarios.
Results
Superior performance of SAMPart3D compared to existing methods, especially in zero-shot settings, demonstrated through quantitative and qualitative results.
Comparative Analysis
Comparisons with other 3D part segmentation methods, highlighting the advantages of SAMPart3D in diverse object segmentation.
Impact and Implications
SAMPart3D offers a groundbreaking approach to 3D part segmentation, showcasing practical applications in material editing, shape manipulation, and hierarchical segmentation based on user interactions.
Key Findings
- SAMPart3D outperforms existing methods in zero-shot 3D part segmentation.
- Introduction of PartObjaverse-Tiny dataset to enhance dataset diversity and complexity.
Limitations
Challenges include the impact of inaccurate masks on final results and the training speed for feature grouping.
Future Directions
Future research opportunities include refining segmentation accuracy, enhancing model efficiency, and exploring real-world applications in 3D object manipulation.
Practical Significance
SAMPart3D's advancements have practical implications in various industries, enabling more efficient and accurate 3D part segmentation for applications like 3D modeling and virtual reality.