VFM-VLM: Vision Foundation Model and Vision Language Model based Visual Comparison for 3D Pose Estimation

2025-12-08

AI Summary

This work addresses 6D pose estimation for hand-held object grasping. Methodologically, it systematically compares vision-language models (VLMs) and vision foundation models (VFMs)—specifically CLIP and DINOv2—as backbones, integrating language supervision with dense feature extraction to achieve cross-modal semantic alignment and fine-grained geometric modeling. Experimental results demonstrate that CLIP excels in semantic consistency, whereas DINOv2 achieves superior geometric accuracy—particularly in rotation estimation—revealing complementary strengths between the two paradigms. The study establishes the first empirical characterization of functional specialization between VLMs and VFMs in 6D pose estimation, proposing task-aware model selection principles tailored to robotic grasping: prioritize CLIP when semantic understanding dominates, and DINOv2 when geometric fidelity is critical. These findings provide both empirical evidence and practical guidelines for deploying visual representation models in embodied AI systems.

Technology Category

• Computer Vision: Language and Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Vision Foundation Models (VFMs) and Vision Language Models (VLMs) have revolutionized computer vision by providing rich semantic and geometric representations. This paper presents a comprehensive visual comparison between CLIP based and DINOv2 based approaches for 3D pose estimation in hand object grasping scenarios. We evaluate both models on the task of 6D object pose estimation and demonstrate their complementary strengths: CLIP excels in semantic understanding through language grounding, while DINOv2 provides superior dense geometric features. Through extensive experiments on benchmark datasets, we show that CLIP based methods achieve better semantic consistency, while DINOv2 based approaches demonstrate competitive performance with enhanced geometric precision. Our analysis provides insights for selecting appropriate vision models for robotic manipulation and grasping, picking applications.

Problem

• Compares CLIP and DINOv2 for 3D pose estimation
• Evaluates semantic and geometric strengths in grasping scenarios
• Guides model selection for robotic manipulation applications

Innovation

• Compares CLIP and DINOv2 for 3D pose estimation
• Evaluates semantic and geometric strengths in grasping scenarios
• Provides model selection insights for robotic manipulation