论文标题
Cendernet：渲染和能力6D姿势估计的中心和曲率表示
CenDerNet: Center and Curvature Representations for Render-and-Compare 6D Pose Estimation
论文作者
论文摘要
我们介绍了Cendernet，这是基于中心和曲率表示的多视图图像的6D姿势估算的框架。寻找反光，纹理对象的精确姿势是工业机器人技术的关键挑战。我们的方法包括三个阶段：首先，一个完全卷积的神经网络可预测每种观点的中心和曲率热图；其次，中心热图用于检测对象实例并找到其3D中心。第三，使用3D中心和曲率热图估算6D对象姿势。通过使用渲染和能力方法共同优化视图的姿势，我们的方法自然处理遮挡和对象对称性。我们表明，Cendernet在两个与行业相关的数据集上优于以前的方法：DIMO和T-less。
We introduce CenDerNet, a framework for 6D pose estimation from multi-view images based on center and curvature representations. Finding precise poses for reflective, textureless objects is a key challenge for industrial robotics. Our approach consists of three stages: First, a fully convolutional neural network predicts center and curvature heatmaps for each view; Second, center heatmaps are used to detect object instances and find their 3D centers; Third, 6D object poses are estimated using 3D centers and curvature heatmaps. By jointly optimizing poses across views using a render-and-compare approach, our method naturally handles occlusions and object symmetries. We show that CenDerNet outperforms previous methods on two industry-relevant datasets: DIMO and T-LESS.
