mlsanityData to 14 Oct 2026

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

arXiv 2512.07215 ↗First posted 8 Dec 2025cs.CV cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 11 of its 18 references that are in the map. First posted on arXiv 8 Dec 2025.

Builds on

11 of 18 references.

References · 11

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  4. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 775
  5. 2022ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimationcited by 116
  6. 2021SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddingscited by 95
  7. 20226-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmarkcited by 86
  8. 2025UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial Referencescited by 9
  9. 2025DINO-VO: A Feature-based Visual Odometry Leveraging a Visual Foundation Modelcited by 5
  10. 2023Industrial Application of 6D Pose Estimation for Robotic Manipulation in Automotive Internal Logisticscited by 3
  11. 2023Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identificationcited by 2

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  3. 2026PhysQuantAgent: An Inference Pipeline of Mass Estimation for Vision-Language Models
  4. 2026Monocular 3D Object Position Estimation with VLMs for Human-Robot Interaction
  5. 2025VLAD-Grasp: Zero-shot Grasp Detection via Vision-Language Models
  6. 2024From Words to Poses: Enhancing Novel Object Pose Estimation with Vision Language Models
  7. 2026VLM3: Vision Language Models Are Native 3D Learners
  8. 2025ManipBench: Benchmarking Vision-Language Models for Low-Level Robot Manipulation