论文标题
部分可观测时空混沌系统的无模型预测
Keypoint Cascade Voting for Point Cloud Based 6DoF Pose Estimation
论文作者
论文摘要
我们提出了一种新颖的关键点投票6DOF对象姿势估计方法，该方法将纯的无序点云几何形状作为无RGB信息的输入。提出的级联关键点投票方法称为RCVPOSE3D，基于一种新型的体系结构，该方法将语义分割的任务与关键点回归的任务区分开，从而提高了两者的有效性并提高了最终性能。该方法还将不同关键点之间的成对约束引入损失函数时，当重新估算数量的损失函数，这被证明是有效的，以及一个新颖的选民自信得分，从而增强了学习和推理阶段。我们提出的RCVPOSE3D在咬合lineMod（74.5％）和YCB-VIDEO（96.9％）数据集上实现了最先进的性能，表现优于现有的基于纯RGB和RGB-D方法，并且与RGB Plus Plus Plus Point Cloine Cloud方法竞争。
We propose a novel keypoint voting 6DoF object pose estimation method, which takes pure unordered point cloud geometry as input without RGB information. The proposed cascaded keypoint voting method, called RCVPose3D, is based upon a novel architecture which separates the task of semantic segmentation from that of keypoint regression, thereby increasing the effectiveness of both and improving the ultimate performance. The method also introduces a pairwise constraint in between different keypoints to the loss function when regressing the quantity for keypoint estimation, which is shown to be effective, as well as a novel Voter Confident Score which enhances both the learning and inference stages. Our proposed RCVPose3D achieves state-of-the-art performance on the Occlusion LINEMOD (74.5%) and YCB-Video (96.9%) datasets, outperforming existing pure RGB and RGB-D based methods, as well as being competitive with RGB plus point cloud methods.
