论文标题
MASKNET：一个完全跨跨的网络，以估计近距离点
MaskNet: A Fully-Convolutional Network to Estimate Inlier Points
论文作者
论文摘要
点云在计算机感知世界的方式上变得重要。从自动驾驶汽车和无人机的激光雷达传感器到我们手机中的飞行和立体声视觉系统的时代，点云无处不在。尽管它们无处不在，但现实世界中的点云通常是由于传感器的局限性或遮挡而丢失的，或者包含传感器噪声或伪影中的无关点。这些问题挑战了需要一对点云之间计算对应关系的算法。因此，本文提出了一个完全跨跨的神经网络，该网络识别一个点云中的指向与另一个点最相似（嵌入式）。当通过我们的网络进行改进时，我们展示了基于学习的和经典的云注册方法的改进。我们在合成和现实世界数据集上演示了这些改进。最后，我们的网络在训练期间看不见的测试数据集上产生了令人印象深刻的结果，从而表现出了普遍性。代码和视频可在https://github.com/vinits5/masknet上找到
Point clouds have grown in importance in the way computers perceive the world. From LIDAR sensors in autonomous cars and drones to the time of flight and stereo vision systems in our phones, point clouds are everywhere. Despite their ubiquity, point clouds in the real world are often missing points because of sensor limitations or occlusions, or contain extraneous points from sensor noise or artifacts. These problems challenge algorithms that require computing correspondences between a pair of point clouds. Therefore, this paper presents a fully-convolutional neural network that identifies which points in one point cloud are most similar (inliers) to the points in another. We show improvements in learning-based and classical point cloud registration approaches when retrofitted with our network. We demonstrate these improvements on synthetic and real-world datasets. Finally, our network produces impressive results on test datasets that were unseen during training, thus exhibiting generalizability. Code and videos are available at https://github.com/vinits5/masknet
