We propose a new method for object pose estimation without CAD models. The previous feature-matching-based method OnePose has shown promising results under a one-shot setting which eliminates the need for CAD models or object-specific training. However, OnePose relies on detecting repeatable image keypoints and is thus prone to failure on low-textured objects. We propose a keypoint-free pose estimation pipeline to remove the need for repeatable keypoint detection. Built upon the detector-free feature matching method LoFTR, we devise a new keypoint-free SfM method to reconstruct a semi-dense point-cloud model for the object. Given a query image for object pose estimation, a 2D-3D matching network directly establishes 2D-3D correspondences between the query image and the reconstructed point-cloud model without first detecting keypoints in the image. Experiments show that the proposed pipeline outperforms existing one-shot CAD-model-free methods by a large margin and is comparable to CAD-model-based methods on LINEMOD even for low-textured objects. We also collect a new dataset composed of 80 sequences of 40 low-textured objects to facilitate future research on one-shot object pose estimation. The supplementary material, code and dataset are available on the project page: https://zju3dv.github.io/onepose_plus_plus/.
翻译:我们为对象提出一个新的不使用 CAD 模型估计方法。 先前基于特性匹配方法 OnePose 在一发式设置下展示了有希望的结果, 消除了对 CAD 模型或对象特定培训的需求。 然而, OnePose 依靠检测可重复的图像关键点, 因而容易在低透质对象上出现故障。 我们提议了一个没有关键点的配置管道, 以消除重复式关键点探测的需要。 在无探测器特征匹配方法LoFTR的基础上, 我们设计了一个新的无关键点SfM 方法, 为对象重建一个半重度点点球模型模型。 鉴于对对象显示估计的查询图像或特定对象培训的查询图像, 2D-3D 匹配网络直接在查询图像和重塑的点球模型之间建立2D-3D对应关系, 而没有首先检测图像中的关键点。 实验显示, 拟议的管道以大幅度超过现有的一发式 CAD- 模式方法, 并且可以与 LINEMOD 低文本对象的 CAD- 模型方法相比。 我们还收集了搜索图纸上的80号 。 。 格式的新的数据设置 。