VFM-VLM: Vision Foundation Model and Vision Language Model based Visual Comparison for 3D Pose Estimation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Pose EstimationSimilar Papers 제목 키워드 기반
Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy
The evaluation of text-generative vision-language models is a challenging yet crucial endeavor. By addressing the limitations of existing Visual Question Answering (VQA) benchmarks and proposing innovative evaluation met…
Language ModelingOpen Vocabulary Attribute DetectionVisual Question AnsweringVisual Question Answering (VQA)CompareBench: A Benchmark for Visual Comparison Reasoning in Vision-Language Models
We introduce CompareBench, a benchmark for evaluating visual comparison reasoning in vision-language models (VLMs), a fundamental yet understudied skill. CompareBench consists of 1000 QA pairs across four tasks: quantity…
Multimodal ReasoningRethinking Electro-Optical Vision Foundation Models for Remote Sensing Retrieval: A Controlled Comparison with Generalist VFM
Vision foundation models have attracted significant attention for their ability to leverage large-scale unlabeled visual data. This advantage is particularly important in remote sensing, where data acquisition is costly …
Image RetrievalVisionGPT: Vision-Language Understanding Agent Using Generalized Multimodal Framework
With the emergence of large language models (LLMs) and vision foundation models, how to combine the intelligence and capacity of these open-sourced or API-available models to achieve open-world visual perception remains …
Language ModelingLanguage ModellingLarge Language ModelQuestion Answering+1InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
The exponential growth of large language models (LLMs) has opened up numerous possibilities for multimodal AGI systems. However, the progress in vision and vision-language foundation models, which are also critical eleme…
Image RetrievalImage-to-Text RetrievalLanguage ModellingLarge Language Model+11