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nn_pts_local_mean = tf.subtract(nn_pts, mean_local, name=tag + 'nn_pts_local_mean') 
dists_local_mean = tf.norm(nn_pts_local_mean, axis=-1, keepdims=True) # dist to local mean

vec = mean_local - nn_pts_center
vec_dist = tf.norm(vec, axis=-1, keepdims =True)
vec_norm = tf.divide(vec, vec_dist)
vec_norm = tf.where(tf.is_nan(vec_norm), tf.ones_like(vec_norm) * 0, vec_norm) 

nn_pts_local_proj = tf.matmul(nn_pts_local, vec_norm, transpose_b=True)
nn_pts_local_proj_dot = tf.divide(nn_pts_local_proj, dists_local)
nn_pts_local_proj_dot = tf.where(tf.is_nan(nn_pts_local_proj_dot), tf.ones_like(nn_pts_local_proj_dot) * 0, nn_pts_local_proj_dot)  # check nan

nn_pts_local_proj_2 = tf.matmul(nn_pts_local_mean, vec_norm, transpose_b=True)
nn_pts_local_proj_dot_2 = tf.divide(nn_pts_local_proj_2, dists_local_mean)
nn_pts_local_proj_dot_2 = tf.where(tf.is_nan(nn_pts_local_proj_dot_2), tf.ones_like(nn_pts_local_proj_dot_2) * 0, nn_pts_local_proj_dot_2)  # check nan

nn_fts = tf.concat([dists_local, dists_local_mean, nn_pts_local_proj_dot, nn_pts_local_proj_dot_2], axis=-1) # d0 d1 a0 a1

# compute indices from nn_pts_local_proj
_**vec = mean_global - nn_pts_center**_
vec_dist = tf.norm(vec, axis=-1, keepdims =True)
vec_norm = tf.divide(vec, vec_dist)
**nn_pts_local_proj = tf.matmul(nn_pts_local, vec_norm, transpose_b=True)**