发表机构
Carl Zeiss AG; Carl Zeiss Digital Innovation（卡尔蔡司股份公司; 卡尔蔡司数字创新公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PIXIE是用于有装配缺陷的未见物体的零样本纹理不变6D姿态估计框架，仅用无纹理3D模型从RGB图像估计姿态，通过合成深度和法线图匹配关键点，基于PnP估计姿态，对光照和纹理变化鲁棒，在基准测试中取得好结果并引入新数据集。
AI 中文摘要
6D姿态估计在机器人技术和计算机视觉中仍是关键挑战，尤其在工业环境中。现有数据驱动方法受资源密集型数据管道、对纹理3D模型的依赖以及对损坏或装配缺陷引起的几何偏差敏感等限制。我们提出PIXIE，一个零样本框架，仅使用无纹理3D模型从RGB图像估计物体6D姿态。通过预训练的跨模态特征匹配器将合成深度和法线图与查询图像匹配，利用匹配关键点进行基于PnP的姿态估计。该方法对光照和纹理变化具有内在鲁棒性，对应滤波处理模型与实物间几何偏差。在广泛使用的公共基准上评估，报告了无特定物体训练的无纹理物体的最新结果，并引入了一个具有装配缺陷、纹理变化和遮挡的新数据集以证明其在现实世界中的适用性。
英文摘要
6D pose estimation remains a key challenge in robotics and computer vision, particularly in industrial environments. The deployment of currently available data-driven methods is often limited by resource-intensive data pipelines, reliance on textured 3D models, and sensitivity to geometric deviations caused by damages or assembly defects. We present PIXIE, a zero-shot framework that estimates the 6D pose of an object from an RGB image using only an untextured 3D model. Synthetic depth and normal maps are rendered from sampled reference viewpoints and matched to the query image via a pretrained cross-modality feature matcher. Matched keypoints are back-projected to obtain 2D--3D correspondences for PnP-based pose estimation. Relying exclusively on geometry makes the method inherently robust to lighting and texture variation, while correspondence filtering handles geometric deviations between the model and physical object. We evaluate on widely-used public benchmarks, reporting state-of-the-art results on texture-less objects without object-specific training, and introduce a novel dataset with assembly defects, texture variations, and occlusion to demonstrate real-world applicability.
CommentsThis work has been accepted for publication in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026. The final published version will be available via IEEE Xplore
