高分辨率开放词汇目标6D位姿估计
High-resolution open-vocabulary object 6D pose estimation
AI总结：
针对6D位姿估计中未见目标泛化难、现有VLM方案性能不足的问题，提出基于VLM的开放词汇架构Horyon，通过文本提示识别目标、提取高分辨率多尺度特征完成跨场景配准，在四个数据集上取得SOTA性能。
摘要：
6D位姿估计任务中向未见目标的泛化极具挑战性。尽管视觉语言模型（Vision-Language Models, VLMs）能够利用自然语言描述支撑未见目标的6D位姿估计，但这类方案的性能不及基于模型的方法。本文提出Horyon——一种基于VLM的开放词汇架构，仅通过文本提示描述，即可解决未见目标的两个场景间的相对位姿估计问题。我们利用文本提示识别场景中的未见目标，随后获取高分辨率多尺度特征，再用这些特征提取跨场景匹配点以完成配准。我们在包含大量未见目标的基准上评估模型，覆盖REAL275、Toyota-Light、Linemod和YCB-Video四个数据集。该方法在所有数据集上均达到当前最优性能，平均召回率较此前的最优方法高出12.6。
英文摘要：
The generalisation to unseen objects in the 6D pose estimation task is very challenging. While Vision-Language Models (VLMs) enable using natural language descriptions to support 6D pose estimation of unseen objects, these solutions underperform compared to model-based methods. In this work we present Horyon, an open-vocabulary VLM-based architecture that addresses relative pose estimation between two scenes of an unseen object, described by a textual prompt only. We use the textual prompt to identify the unseen object in the scenes and then obtain high-resolution multi-scale features. These features are used to extract cross-scene matches for registration. We evaluate our model on a benchmark with a large variety of unseen objects across four datasets, namely REAL275, Toyota-Light, Linemod, and YCB-Video. Our method achieves state-of-the-art performance on all datasets, outperforming by 12.6 in Average Recall the previous best-performing approach.
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。
