Jialin Tang
ABSTRACT
Many significant progresses have been made in the field of deep learning. This paper mainly discusses the 3D Match point cloud registration method based on deep learning and its improvement. The method introduced in this article is divided into four steps. The first is to obtain point cloud data. This step uses a bilateral filtering algorithm, which plays a good role in removing noise points from point clouds. The second step is to use 3d match to register key points. This step uses the truncated distance function (TDF) to perform preliminary processing on the point cloud data, and input the Siamese network matching with metric learning to learn the features of the point cloud. The third step eliminates the wrong point pair, this step uses the classic RANSAC algorithm. The fourth step is similarity measurement. The 3D Match network will output a set of 512-dimensional feature vectors, and the spatial dimension is relatively high. Therefore, a cosine similarity that is more suitable for multi-dimensional feature similarity measurement is used to replace the commonly used Euclidean distance.
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