diff --git a/pytorch3d/renderer/mesh/rasterizer.py b/pytorch3d/renderer/mesh/rasterizer.py
index 1a4d4abc..3ea2ae16 100644
--- a/pytorch3d/renderer/mesh/rasterizer.py
+++ b/pytorch3d/renderer/mesh/rasterizer.py
@@ -92,23 +92,20 @@ class MeshRasterizer(nn.Module):
             msg = "Cameras must be specified either at initialization \
                 or in the forward pass of MeshRasterizer"
             raise ValueError(msg)
-
         verts_world = meshes_world.verts_padded()
-        verts_world_packed = meshes_world.verts_packed()
-        verts_screen = cameras.transform_points(verts_world, **kwargs)
 
         # NOTE: Retaining view space z coordinate for now.
         # TODO: Revisit whether or not to transform z coordinate to [-1, 1] or
         # [0, 1] range.
-        view_transform = get_world_to_view_transform(R=cameras.R, T=cameras.T)
-        verts_view = view_transform.transform_points(verts_world)
+        verts_view = cameras.get_world_to_view_transform(**kwargs).transform_points(
+            verts_world
+        )
+        verts_screen = cameras.get_projection_transform(**kwargs).transform_points(
+            verts_view
+        )
         verts_screen[..., 2] = verts_view[..., 2]
-
-        # Offset verts of input mesh to reuse cached padded/packed calculations.
-        pad_to_packed_idx = meshes_world.verts_padded_to_packed_idx()
-        verts_screen_packed = verts_screen.view(-1, 3)[pad_to_packed_idx, :]
-        verts_packed_offset = verts_screen_packed - verts_world_packed
-        return meshes_world.offset_verts(verts_packed_offset)
+        meshes_screen = meshes_world.update_padded(new_verts_padded=verts_screen)
+        return meshes_screen
 
     def forward(self, meshes_world, **kwargs) -> Fragments:
         """
diff --git a/tests/bm_mesh_rasterizer_transform.py b/tests/bm_mesh_rasterizer_transform.py
new file mode 100644
index 00000000..97672504
--- /dev/null
+++ b/tests/bm_mesh_rasterizer_transform.py
@@ -0,0 +1,45 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+
+from itertools import product
+
+import torch
+from fvcore.common.benchmark import benchmark
+from pytorch3d.renderer.cameras import OpenGLPerspectiveCameras, look_at_view_transform
+from pytorch3d.renderer.mesh.rasterizer import MeshRasterizer
+from pytorch3d.utils.ico_sphere import ico_sphere
+
+
+def rasterize_transform_with_init(num_meshes: int, ico_level: int = 5, device="cuda"):
+    # Init meshes
+    sphere_meshes = ico_sphere(ico_level, device).extend(num_meshes)
+    # Init transform
+    R, T = look_at_view_transform(1.0, 0.0, 0.0)
+    cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)
+    # Init rasterizer
+    rasterizer = MeshRasterizer(cameras=cameras)
+
+    torch.cuda.synchronize()
+
+    def raster_fn():
+        rasterizer.transform(sphere_meshes)
+        torch.cuda.synchronize()
+
+    return raster_fn
+
+
+def bm_mesh_rasterizer_transform() -> None:
+    if torch.cuda.is_available():
+        kwargs_list = []
+        num_meshes = [1, 8]
+        ico_level = [0, 1, 3, 4]
+        test_cases = product(num_meshes, ico_level)
+        for case in test_cases:
+            n, ic = case
+            kwargs_list.append({"num_meshes": n, "ico_level": ic})
+        benchmark(
+            rasterize_transform_with_init,
+            "MESH_RASTERIZER",
+            kwargs_list,
+            warmup_iters=1,
+        )