基于视觉基础模型和视觉语言模型的3D姿态估计视觉比较：VFM-VLM
VFM-VLM: Vision Foundation Model and Vision Language Model based Visual Comparison for 3D Pose Estimation
AI总结：
本文比较了基于CLIP和DINOv2的视觉模型在3D姿态估计中的性能，展示了两者在语义理解和几何精度上的互补优势，为机器人抓取应用提供了模型选择依据。
摘要：
视觉基础模型（VFMs）和视觉语言模型（VLMs）通过提供丰富的语义和几何表示革新了计算机视觉。本文全面比较了基于CLIP和DINOv2的两种方法在手部物体抓取场景中的3D姿态估计任务。我们评估了两种模型在6D物体姿态估计任务上的表现，并展示了它们的互补优势：CLIP通过语言定位在语义理解上表现出色，而DINOv2提供更优的密集几何特征。通过在基准数据集上的广泛实验，我们证明基于CLIP的方法在语义一致性方面表现更好，而基于DINOv2的方法在几何精度上具有竞争力。我们的分析为选择适合机器人操作和抓取的视觉模型提供了见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
