Realtime 3-D object detection and 6-DOF pose estimation in clutter background is crucial for intelligent manufacturing, for example, robot feeding and assembly, where robustness and efficiency are the two most desirable goals. Especially for various metal parts with a textless surface, it is hard for most state of the arts to extract robust feature from the clutter background with various occlusions. To overcome this, in this paper, we propose an online 3-D object detection and pose estimation method to overcome self-occlusion for textureless objects. For feature representation, we only adopt the raw 3-D point clouds with normal cues to define our local reference frame and we automatically learn the compact 3-D feature from the simple local normal statistics via autoencoder. For a similarity search, a new basis buffer k-d tree method is designed without suffering branch divergence; therefore, ours can maximize the GPU parallel processing capabilities especially in practice. We then generate the hypothesis candidates via the hough voting, filter the false hypotheses, and refine the pose estimation via the iterative closest point strategy. For the experiments, we build a new 3-D dataset including industrial objects with heavy self-occlusions and conduct various comparisons with the state of the arts to justify the effectiveness and efficiency of our method.

中文翻译：

---

加速智能制造中针对自遮挡的 3D 无纹理对象识别


杂波背景下的实时 3D 物体检测和 6-DOF 位姿估计对于智能制造至关重要，例如机器人送料和装配，其中鲁棒性和效率是两个最理想的目标。特别是对于具有无文本表面的各种金属零件，大多数现有技术很难从具有各种遮挡的杂乱背景中提取鲁棒的特征。为了克服这个问题，在本文中，我们提出了一种在线 3D 对象检测和姿态估计方法来克服无纹理对象的自遮挡。对于特征表示，我们仅采用具有法线提示的原始 3D 点云来定义局部参考系，并通过自动编码器从简单的局部法线统计中自动学习紧凑的 3D 特征。对于相似性搜索，设计了一种新的基础缓冲区kd树方法，而不会受到分支发散的影响；因此，我们可以最大限度地发挥GPU并行处理能力，尤其是在实践中。然后，我们通过霍夫投票生成候选假设，过滤错误假设，并通过迭代最近点策略细化姿态估计。在实验中，我们构建了一个新的 3D 数据集，其中包括具有严重自遮挡的工业对象，并与现有技术进行各种比较，以证明我们方法的有效性和效率。