diff --git a/paddle/operators/math/vol2col.cc b/paddle/operators/math/vol2col.cc
index d383ee81526ae270abd931f18e51b02c9ca90f7a..bd509a94f3fb176402b3dead54c3c0c933f24307 100644
--- a/paddle/operators/math/vol2col.cc
+++ b/paddle/operators/math/vol2col.cc
@@ -181,6 +181,7 @@ class Col2VolFunctor<platform::CPUPlace, T> {
                   ((cIm * input_depth + d_pad) * input_height + h_pad) *
                       input_width +
                   w_pad;
+
               int col_idx =
                   ((c * output_depth + d) * output_height + h) * output_width +
                   w;
diff --git a/python/paddle/v2/framework/tests/test_conv3d_op.py b/python/paddle/v2/framework/tests/test_conv3d_op.py
index 44c192f58d25f8ddaa38d2ba7c7c19b9a5bd7dc1..934ea46437d67b78309a86a2779e0c6577399136 100644
--- a/python/paddle/v2/framework/tests/test_conv3d_op.py
+++ b/python/paddle/v2/framework/tests/test_conv3d_op.py
@@ -10,27 +10,40 @@ def conv3d_forward_naive(input, filter, group, conv_param):
     assert np.mod(out_c, group) == 0
     sub_out_c = out_c / group
 
-    stride, pad = conv_param['stride'], conv_param['pad']
-    out_d = 1 + (in_d + 2 * pad[0] - f_h) / stride[0]
-    out_h = 1 + (in_h + 2 * pad[1] - f_h) / stride[1]
-    out_w = 1 + (in_w + 2 * pad[2] - f_w) / stride[2]
+    stride, pad, dilation = conv_param['stride'], conv_param['pad'], conv_param[
+        'dilations']
+
+    out_d = 1 + (in_d + 2 * pad[0] - (dilation[0] * (f_d - 1) + 1)) / stride[0]
+    out_h = 1 + (in_h + 2 * pad[1] - (dilation[1] * (f_h - 1) + 1)) / stride[1]
+    out_w = 1 + (in_w + 2 * pad[2] - (dilation[2] * (f_w - 1) + 1)) / stride[2]
+
     out = np.zeros((in_n, out_c, out_d, out_h, out_w))
 
+    d_bolck_d = (dilation[0] * (f_d - 1) + 1)
+    d_bolck_h = (dilation[1] * (f_h - 1) + 1)
+    d_bolck_w = (dilation[2] * (f_w - 1) + 1)
+
     input_pad = np.pad(input, ((0, ), (0, ), (pad[0], ), (pad[1], ),
                                (pad[2], )),
                        mode='constant',
                        constant_values=0)
+
+    filter_dilation = np.zeros((out_c, f_c, d_bolck_d, d_bolck_h, d_bolck_w))
+    filter_dilation[:, :, 0:d_bolck_d:dilation[0], 0:d_bolck_h:dilation[1], 0:
+                    d_bolck_w:dilation[2]] = filter
+
     for d in range(out_d):
         for i in range(out_h):
             for j in range(out_w):
                 for g in range(group):
                     input_pad_masked = \
                         input_pad[:, g * f_c:(g + 1) * f_c,
-                        d * stride[0]:d * stride[0] + f_d,
-                        i * stride[1]:i * stride[1] + f_h,
-                        j * stride[2]:j * stride[2] + f_w]
-                    f_sub = filter[g * sub_out_c:(g + 1) *
-                                   sub_out_c, :, :, :, :]
+                        d * stride[0]:d * stride[0] + d_bolck_d,
+                        i * stride[1]:i * stride[1] + d_bolck_h,
+                        j * stride[2]:j * stride[2] + d_bolck_w]
+
+                    f_sub = filter_dilation[g * sub_out_c:(g + 1) *
+                                            sub_out_c, :, :, :, :]
                     for k in range(sub_out_c):
                         out[:, g * sub_out_c + k, d, i, j] = \
                             np.sum(input_pad_masked * f_sub[k, :, :, :, :],
@@ -43,9 +56,14 @@ class TestConv3dOp(OpTest):
     def setUp(self):
         self.init_group()
         self.init_op_type()
+        self.init_dilation()
         self.init_test_case()
 
-        conv3d_param = {'stride': self.stride, 'pad': self.pad}
+        conv3d_param = {
+            'stride': self.stride,
+            'pad': self.pad,
+            'dilations': self.dilations
+        }
         input = np.random.random(self.input_size).astype("float32")
         filter = np.random.random(self.filter_size).astype("float32")
         output = conv3d_forward_naive(input, filter, self.groups,
@@ -55,7 +73,8 @@ class TestConv3dOp(OpTest):
         self.attrs = {
             'strides': self.stride,
             'paddings': self.pad,
-            'groups': self.groups
+            'groups': self.groups,
+            'dilations': self.dilations
         }
         self.outputs = {'Output': output}
 
@@ -88,6 +107,9 @@ class TestConv3dOp(OpTest):
         f_c = self.input_size[1] / self.groups
         self.filter_size = [6, f_c, 3, 3, 3]
 
+    def init_dilation(self):
+        self.dilations = [1, 1, 1]
+
     def init_group(self):
         self.groups = 1
 
@@ -104,27 +126,47 @@ class TestCase1(TestConv3dOp):
         f_c = self.input_size[1] / self.groups
         self.filter_size = [6, f_c, 3, 3, 3]
 
-    def init_group(self):
-        self.groups = 1
 
-    def init_op_type(self):
-        self.op_type = "conv3d"
+class TestWithGroup1(TestConv3dOp):
+    def init_group(self):
+        self.groups = 3
 
 
-class TestWithGroup1(TestConv3dOp):
+class TestWithGroup2(TestCase1):
     def init_group(self):
         self.groups = 3
 
-    def init_op_type(self):
-        self.op_type = "conv3d"
 
+class TestWith1x1(TestConv3dOp):
+    def init_test_case(self):
+        self.pad = [0, 0, 0]
+        self.stride = [1, 1, 1]
+        self.input_size = [2, 3, 4, 4, 4]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] / self.groups
+        self.filter_size = [6, f_c, 1, 1, 1]
+
+    def init_dilation(self):
+        self.dilations = [1, 1, 1]
 
-class TestWithGroup2(TestCase1):
     def init_group(self):
         self.groups = 3
 
-    def init_op_type(self):
-        self.op_type = "conv3d"
+
+class TestWithDilation(TestConv3dOp):
+    def init_test_case(self):
+        self.pad = [0, 0, 0]
+        self.stride = [1, 1, 1]
+        self.input_size = [2, 3, 6, 6, 6]  # NCDHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] / self.groups
+        self.filter_size = [6, f_c, 2, 2, 2]
+
+    def init_dilation(self):
+        self.dilations = [2, 2, 2]
+
+    def init_group(self):
+        self.groups = 3
 
 
 if __name__ == '__main__':