updated readme and small modification to utils

PatrickFeng · Jan 16, 2018 · 52a3882 · 52a3882
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Showing 3 changed files with 6 additions and 3 deletions.
diff --git a/README.md b/README.md
@@ -45,6 +45,8 @@ To train a model to classify point clouds sampled from ModelNet40 shapes:
         cd classification
         python train.py
 
+Note that for the XYZ+normal experiments: (1) 5000 points are used and (2) a further random data dropout augmentation is used during training (see commented line after `augment_batch_data` in `train.py` and (3) the model architecture is updated such that the `nsample=128` in the first two set abstraction levels, which is suited for the larger point density in 5000-point samplings.
+
 #### Object Part Segmentation
 
 To train a model to segment object parts for ShapeNet models:

diff --git a/classification/train.py b/classification/train.py
@@ -209,6 +209,7 @@ def train_one_epoch(sess, ops, train_writer):
         end_idx = (batch_idx+1) * BATCH_SIZE
         batch_data, batch_label = get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx)
         aug_data = augment_batch_data(batch_data)
+        #aug_data = provider.random_point_dropout(aug_data)
         feed_dict = {ops['pointclouds_pl']: aug_data,
                      ops['labels_pl']: batch_label,
                      ops['is_training_pl']: is_training,}

diff --git a/utils/provider.py b/utils/provider.py
@@ -56,7 +56,7 @@ def rotate_point_cloud_with_normal(batch_xyz_normal):
         shape_pc = batch_xyz_normal[k,:,0:3]
         shape_normal = batch_xyz_normal[k,:,3:6]
         batch_xyz_normal[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
-        batch_xyz_normal[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), np.linalg.inv(rotation_matrix.T))
+        batch_xyz_normal[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), rotation_matrix)
     return batch_xyz_normal
 
 def rotate_perturbation_point_cloud_with_normal(batch_data, angle_sigma=0.06, angle_clip=0.18):
@@ -82,7 +82,7 @@ def rotate_perturbation_point_cloud_with_normal(batch_data, angle_sigma=0.06, an
         shape_pc = batch_data[k,:,0:3]
         shape_normal = batch_data[k,:,3:6]
         rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), R)
-        rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), np.linalg.inv(R.T))
+        rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), R)
     return rotated_data
 
 
@@ -123,7 +123,7 @@ def rotate_point_cloud_by_angle_with_normal(batch_data, rotation_angle):
         shape_pc = batch_data[k, ...]
         shape_normal = batch_data[k,:,3:6]
         rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
-        rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1,3)), np.linalg.inv(rotation_matrix.T))
+        rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1,3)), rotation_matrix)
     return rotated_data