论文标题
EPO：估计具有对称物体的6D姿势
EPOS: Estimating 6D Pose of Objects with Symmetries
论文作者
论文摘要
我们提出了一种新方法，用于估计来自单个RGB输入图像的可用3D模型的6D姿势。该方法适用于广泛的对象，包括具有全球或部分对称性的具有挑战性的对象。对象由紧凑的表面碎片表示，这些片段允许以系统的方式处理对称性。密集采样像素和片段之间的对应关系是使用编码器码头网络预测的。在每个像素上，网络预测：（i）每个对象的存在的概率，（ii）给定对象的存在的片段的概率，以及（iii）每个片段上的精确3D位置。每个像素选择相应的3D位置数量，并使用PNP-RANSAC算法的稳健和有效变体估算可能的多个对象实例的姿势。在2019年BOP挑战赛中，该方法在T-less和LM-O数据集上的表现优于所有RGB，大多数RGB-D和D方法。在YCB-V数据集上，它比所有竞争对手都优于第二好的RGB方法。源代码为：cmp.felk.cvut.cz/epos。
We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image. The method is applicable to a broad range of objects, including challenging ones with global or partial symmetries. An object is represented by compact surface fragments which allow handling symmetries in a systematic manner. Correspondences between densely sampled pixels and the fragments are predicted using an encoder-decoder network. At each pixel, the network predicts: (i) the probability of each object's presence, (ii) the probability of the fragments given the object's presence, and (iii) the precise 3D location on each fragment. A data-dependent number of corresponding 3D locations is selected per pixel, and poses of possibly multiple object instances are estimated using a robust and efficient variant of the PnP-RANSAC algorithm. In the BOP Challenge 2019, the method outperforms all RGB and most RGB-D and D methods on the T-LESS and LM-O datasets. On the YCB-V dataset, it is superior to all competitors, with a large margin over the second-best RGB method. Source code is at: cmp.felk.cvut.cz/epos.
