Hello there, I've run the code for 2d nonrigid point clouds registration, the performance is quite amazing with the default peremeters setting for source and target points. However, when I try to add outliers by setting outlier_ratio=1.1 to source points, as a result, the number of points in each source pointcloud is different, and the algorithm crashes at
write_to_tfrecords({"source": np.asanyarray([i.T for i in a.source_list])[np.random.choice(range(test_num), test_num)], "target": np.asanyarray([i.T for i in a.target_list])}, "temp_test_1.tfrecords")
more excatly, it happens becuase np.asanyarray cannot deal with elements with different shapes.
I read your paper and it's said that outliers are considered and the result seems great, so I'm here to look for some help, how can I modify the codes in order to cope with this problem?
Hello there, I've run the code for 2d nonrigid point clouds registration, the performance is quite amazing with the default peremeters setting for source and target points. However, when I try to add outliers by setting outlier_ratio=1.1 to source points, as a result, the number of points in each source pointcloud is different, and the algorithm crashes at
write_to_tfrecords({"source": np.asanyarray([i.T for i in a.source_list])[np.random.choice(range(test_num), test_num)], "target": np.asanyarray([i.T for i in a.target_list])}, "temp_test_1.tfrecords")more excatly, it happens becuase np.asanyarray cannot deal with elements with different shapes.
I read your paper and it's said that outliers are considered and the result seems great, so I'm here to look for some help, how can I modify the codes in order to cope with this problem?