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View Code? Open in Web Editor NEWSpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
License: MIT License
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
License: MIT License
where aer your evaluate.py?
I wonder if you can provide the pretraind modle about ModelNet40?
Thamks!
Is the evaluation of classification same as pointnet2(multi vote)?
Thank you very much?
It is weird that you implement the taylor polynomials mentioned in your paper in this way:
w_x = tf.tile(_variable_on_cpu('weight_x', shape, initializer), [batch_size, num_point, K_knn, 1])
w_y = tf.tile(_variable_on_cpu('weight_y', shape, initializer), [batch_size, num_point, K_knn, 1])
w_z = tf.tile(_variable_on_cpu('weight_z', shape, initializer), [batch_size, num_point, K_knn, 1])
...
g1 = w_x * X + w_y * Y + w_z * Z + w_xyz * X * Y * Z
g2 = w_xy * X * Y + w_yz * Y * Z + w_xz * X * Z + biases
g3 = w_xx * X * X + w_yy * Y * Y + w_zz * Z * Z
g4 = w_xxy * X * X * Y + w_xyy * X * Y * Y + w_xxz * X * X * Z
g5 = w_xzz * X * Z * Z + w_yyz * Y * Y * Z + w_yzz * Y * Z * Z
g6 = w_xxx * X * X * X + w_yyy * Y * Y * Y + w_zzz * Z * Z * Z
g_d = g1 + g2 + g3 + g4 + g5 + g6
I think it equal if you concatenate of [X, Y, Z, XX, YY, ...., XYZ] and multiply a [20xtaylor_channel] matrix (equal to a fully connected layer).
Do I miss something or understanding wrong?
could you please share the evalution code?thanks!
It seems that the download link of the ModelNet40 dataset you provided only contains xyz coordinates.
Can you post the link of the dataset with normals?
I get this message (tf_sampling_so.so not found ) when I try to run the train.py file
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