对比CLIP与DINOv2在3D抓握姿态估计中的表现，指导机器人抓取选型。
VFM-VLM: Vision Foundation Model and Vision Language Model based Visual Comparison for 3D Pose Estimation
- 用CLIP和DINOv2进行3D姿态估计的视觉对比，结合语义与几何特征。
- CLIP在语义一致性上更优，DINOv2在几何精度上更具优势。
- 为机器人抓取任务提供模型选择依据，适合工业自动化场景。
视觉基础模型（VFMs）和视觉语言模型（VLMs）通过提供丰富的语义与几何表征，革新了计算机视觉。本文系统比较了基于CLIP与DINOv2的方法在手-物抓握场景下的3D姿态估计性能。在6D物体姿态估计任务上，我们评估两种模型，发现其具有互补优势：CLIP凭借语言对齐，在语义理解上表现优异；而DINOv2则提供更密集的几何特征。在基准数据集上的大量实验表明，基于CLIP的方法在语义一致性上更优，而基于DINOv2的方法在几何精度上表现竞争性。本分析为机器人操纵与抓取应用中视觉模型的选择提供了重要见解。
原文摘要 · Abstract (English)
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.
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