无需特征匹配，通过多视角几何感知实现未知物体6D姿态精准估计
PoseGAM: Robust Unseen Object Pose Estimation via Geometry-Aware Multi-View Reasoning
- 基于多视角与几何感知机制，直接从图像预测姿态
- 在多个基准上平均精度提升5.1%，个别数据集最高提升17.6%
- 适用于工业检测、机器人抓取等未知物体识别场景
6D物体姿态估计旨在预测物体相对于相机的变换，对未见过的物体仍具挑战性。现有方法通常依赖于查询图像与物体模型或模板图像之间的显式特征匹配。本文提出PoseGAM，一种基于几何感知的多视角框架，可直接从查询图像和多张模板图像中预测物体姿态，无需显式匹配。该方法基于最新的多视角基础模型架构，通过两种互补机制融入物体几何信息：基于点的显式几何结构与几何表示网络学习的特征。此外，我们构建了一个包含超过19万种物体、覆盖多样环境条件的大规模合成数据集，以增强鲁棒性和泛化能力。在多个基准上的广泛评估表明，本方法达到当前最优性能，平均姿态召回率（AR）相比先前方法提升5.1%，个别数据集最高提升17.6%，展现出对未见物体的强大泛化能力。
原文摘要 · Abstract (English)
6D object pose estimation, which predicts the transformation of an object relative to the camera, remains challenging for unseen objects. Existing approaches typically rely on explicitly constructing feature correspondences between the query image and either the object model or template images. In this work, we propose PoseGAM, a geometry-aware multi-view framework that directly predicts object pose from a query image and multiple template images, eliminating the need for explicit matching. Built upon recent multi-view-based foundation model architectures, the method integrates object geometry information through two complementary mechanisms: explicit point-based geometry and learned features from geometry representation networks. In addition, we construct a large-scale synthetic dataset containing more than 190k objects under diverse environmental conditions to enhance robustness and generalization. Extensive evaluations across multiple benchmarks demonstrate our state-of-the-art performance, yielding an average AR improvement of 5.1% over prior methods and achieving up to 17.6% gains on individual datasets, indicating strong generalization to unseen objects. Project page: https://windvchen.github.io/PoseGAM/ .
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。