diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index 0e6d4caae0ee..8ed94c2a81c9 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -1071,13 +1071,17 @@ def maxpool_1d(self, inputs, input_types):
     def maxpool_3d(self, inputs, input_types):
         data = inputs[0]
 
+        need_squeeze = False
+        if len(self.get_dims(data)) == 4:
+            need_squeeze = True
+            data = _op.expand_dims(data, 0)
         pool_size = inputs[1]
         strides = inputs[2] if inputs[2] else pool_size
         padding = inputs[3]
         dilation = inputs[4]
         ceil_mode = int(inputs[5])
 
-        return _op.nn.max_pool3d(
+        res = _op.nn.max_pool3d(
             data,
             pool_size=pool_size,
             strides=strides,
@@ -1085,6 +1089,7 @@ def maxpool_3d(self, inputs, input_types):
             padding=padding,
             ceil_mode=ceil_mode,
         )
+        return res if not need_squeeze else _op.squeeze(res, [0])
 
     def hardtanh(self, inputs, input_types):
         a = inputs[0]
diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index 3828412de054..a030c5141a31 100755
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -906,13 +906,17 @@ def forward(self, *args):
 def test_forward_maxpool3d():
     """test_forward_maxpool3d"""
     torch.set_grad_enabled(False)
-    input_shape = [1, 3, 10, 10, 10]
-    input_data = torch.rand(input_shape).float()
+    for input_shape in [(1, 3, 10, 10, 10), (3, 10, 10, 10)]:
+        input_data = torch.rand(input_shape).float()
 
-    verify_model(torch.nn.MaxPool3d(kernel_size=[1, 1, 1]).eval(), input_data)
-    verify_model(torch.nn.MaxPool3d(kernel_size=[2, 2, 2], dilation=[1, 2, 3]).eval(), input_data)
-    verify_model(torch.nn.MaxPool3d(kernel_size=[10, 10, 10]).eval(), input_data)
-    verify_model(torch.nn.MaxPool3d(kernel_size=[4, 4, 4], padding=2, stride=2).eval(), input_data)
+        verify_model(torch.nn.MaxPool3d(kernel_size=[1, 1, 1]).eval(), input_data)
+        verify_model(
+            torch.nn.MaxPool3d(kernel_size=[2, 2, 2], dilation=[1, 2, 3]).eval(), input_data
+        )
+        verify_model(torch.nn.MaxPool3d(kernel_size=[10, 10, 10]).eval(), input_data)
+        verify_model(
+            torch.nn.MaxPool3d(kernel_size=[4, 4, 4], padding=2, stride=2).eval(), input_data
+        )
 
     # A functional variant (default strides = None case)
     class MaxPool3D(Module):