diff --git a/README.md b/README.md
index 04a1528c..04cffa61 100644
--- a/README.md
+++ b/README.md
@@ -56,10 +56,15 @@ After training, to evaluate the classification accuracies (with optional multi-a
 
         python evaluate.py --num_votes 12 
 
+After training, to predict class of a single object (with optional multi-angle voting and visualization):
+
+        python predict_cls.py --path path_to_pointcloud.txt --num_votes 12 -v 
+
 <i>Side Note:</i> For the XYZ+normal experiment reported in our paper: (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.
 
 To use normal features for classification: You can get our sampled point clouds of ModelNet40 (XYZ and normal from mesh, 10k points per shape) <a href="https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip">here (1.6GB)</a>. Move the uncompressed data folder to `data/modelnet40_normal_resampled`
 
+
 #### Object Part Segmentation
 
 To train a model to segment object parts for ShapeNet models:
diff --git a/predict_cls.py b/predict_cls.py
new file mode 100644
index 00000000..f8e27137
--- /dev/null
+++ b/predict_cls.py
@@ -0,0 +1,132 @@
+'''
+    Predict class of single pointcloud data.
+'''
+import tensorflow as tf
+import numpy as np
+import argparse
+import importlib
+import os
+import sys
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+ROOT_DIR = BASE_DIR
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(ROOT_DIR, 'models'))
+sys.path.append(os.path.join(ROOT_DIR, 'utils'))
+
+import provider
+
+from pprint import pprint
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
+parser.add_argument('--model', default='pointnet2_cls_ssg', help='Model name. [default: pointnet2_cls_ssg]')
+parser.add_argument('--num_point', type=int, default=1024, help='Point Number [256/512/1024/2048] [default: 1024]')
+parser.add_argument('--model_path', default='log/model.ckpt', help='model checkpoint file path [default: log/model.ckpt]')
+parser.add_argument('--dump_dir', default='dump', help='dump folder path [dump]')
+parser.add_argument('--normal', action='store_true', help='Whether to use normal information')
+parser.add_argument('--num_votes', type=int, default=1, help='Aggregate classification scores from multiple rotations [default: 1]')
+parser.add_argument('-v', '--visualize', action='store_true', help='Visualize input pointcloud data')
+parser.add_argument('--path', help='Path of pointcloud txt')
+
+FLAGS = parser.parse_args()
+
+NUM_POINT = FLAGS.num_point
+MODEL_PATH = FLAGS.model_path
+GPU_INDEX = FLAGS.gpu
+MODEL = importlib.import_module(FLAGS.model) # import network module
+DUMP_DIR = FLAGS.dump_dir
+VISUALIZE = FLAGS.visualize
+if not os.path.exists(DUMP_DIR): os.mkdir(DUMP_DIR)
+LOG_FOUT = open(os.path.join(DUMP_DIR, 'log_evaluate.txt'), 'w')
+LOG_FOUT.write(str(FLAGS)+'\n')
+
+NUM_CLASSES = 40
+SHAPE_NAMES = [line.rstrip() for line in \
+    open(os.path.join(ROOT_DIR, 'data/modelnet40_ply_hdf5_2048/shape_names.txt'))] 
+
+
+PC_PATH = FLAGS.path
+# Get first n dimensions, must change with normal flag
+npoints = 3 
+
+
+def log_string(out_str):
+    LOG_FOUT.write(out_str+'\n')
+    LOG_FOUT.flush()
+    print(out_str)
+
+def evaluate():
+     
+    with tf.device('/gpu:'+str(GPU_INDEX)):
+        pointclouds_pl, labels_pl = MODEL.placeholder_inputs(1, NUM_POINT)
+        is_training_pl = tf.placeholder(tf.bool, shape=())
+
+        # simple model
+        pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl)
+        
+        # Add ops to save and restore all the variables.
+        saver = tf.train.Saver()
+        
+    # Create a session
+    config = tf.ConfigProto()
+    config.gpu_options.allow_growth = True
+    config.allow_soft_placement = True
+    config.log_device_placement = False
+    sess = tf.Session(config=config)
+
+    # Restore variables from disk.
+    saver.restore(sess, MODEL_PATH)
+    log_string("Model restored.")
+
+    ops = {'pointclouds_pl': pointclouds_pl,
+           'labels_pl': labels_pl,
+           'is_training_pl': is_training_pl,
+           'pred': pred
+    }
+
+    # Load pointcloud data from txt file
+    point_set = np.loadtxt(PC_PATH, delimiter=',').astype(np.float32)
+    # Get indexes for random points from pointcloud
+    random_idx = np.random.randint(point_set.shape[0], size=1024) 
+
+    #point_set = point_set[random_idx,0:npoints]
+    point_set = point_set[:NUM_POINT, 0:npoints]
+    point_set = np.array([point_set])
+
+    pred_one(sess, ops, point_set)
+
+def pred_one(sess, ops, pointcloud_data):
+    is_training = False
+    num_votes = FLAGS.num_votes
+
+    pred_val_sum = np.zeros((1, NUM_CLASSES))
+
+    for vote_idx in range(num_votes):
+
+        rotation = vote_idx/float(num_votes) * np.pi * 2
+        rotated_data = provider.rotate_point_cloud_by_angle(pointcloud_data, rotation)
+
+        feed_dict = {ops['pointclouds_pl']: rotated_data,
+                        ops['is_training_pl']: is_training}
+
+        pred_val = sess.run([ops['pred']], feed_dict=feed_dict)[0]
+        pred_val_sum += pred_val
+        idx = np.argmax(pred_val)
+
+        print("Predicted shape as: '{}' with rotation: {}".format(SHAPE_NAMES[idx], np.degrees(rotation)) )
+    
+    final_idx = np.argmax(pred_val_sum)
+    print("Final prediction:", SHAPE_NAMES[final_idx])
+
+    if VISUALIZE:
+        from show3d_balls import showpoints
+
+        showpoints(pointcloud_data[0])
+
+
+if __name__=='__main__':
+    with tf.Graph().as_default():
+        evaluate()
+    LOG_FOUT.close()
+