The challenges of learning a robust 6D pose function lie in 1) severe
occlusion and 2) systematic noises in depth images. Inspired by the success of
point-pair features, the goal of this paper is to recover the 6D pose of an
object instance segmented from RGB-D images by locally matching pairs of
oriented points between the model and camera space. To this end, we propose a
novel Bi-directional Correspondence Mapping Network (BiCo-Net) to first
generate point clouds guided by a typical pose regression, which can thus
incorporate pose-sensitive information to optimize generation of local
coordinates and their normal vectors. As pose predictions via geometric
computation only rely on one single pair of local oriented points, our BiCo-Net
can achieve robustness against sparse and occluded point clouds. An ensemble of
redundant pose predictions from locally matching and direct pose regression
further refines final pose output against noisy observations. Experimental
results on three popularly benchmarking datasets can verify that our method can
achieve state-of-the-art performance, especially for the more challenging
severe occluded scenes. Source codes are available at
this https URL

本研究提出了一种双向对应映射网络 (BiCo-Net)，使用定向点对进行局部匹配，并结合姿态感知信息，生成并优化局部坐标及其法向量，以恢复起源于 RGB-D 图像、被分割成对象实例的物体的 6D 姿态，从而实现克服因严重遮挡和深度图像中的系统噪声而带来的挑战。实验结果表明，该方法在三个流行的基准数据集上可实现最先进的性能，尤其适用于更具挑战性的严重遮挡场景。