diff --git a/pytorch3d/csrc/marching_cubes/marching_cubes.cu b/pytorch3d/csrc/marching_cubes/marching_cubes.cu
index 44d50934..8d05ec80 100644
--- a/pytorch3d/csrc/marching_cubes/marching_cubes.cu
+++ b/pytorch3d/csrc/marching_cubes/marching_cubes.cu
@@ -382,6 +382,44 @@ __global__ void GenerateFacesKernel(
   } // end for grid-strided kernel
 }
 
+// ATen/Torch does not have an exclusive-scan operator. Additionally, in the
+// code below we need to get the "total number of items to work on" after
+// a scan, which with an inclusive-scan would simply be the value of the last
+// element in the tensor.
+//
+// This utility function hits two birds with one stone, by running
+// an inclusive-scan into a right-shifted view of a tensor that's
+// allocated to be one element bigger than the input tensor.
+//
+// Note; return tensor is `int64_t` per element, even if the input
+// tensor is only 32-bit. Also, the return tensor is one element bigger
+// than the input one.
+//
+// Secondary optional argument is an output argument that gets the
+// value of the last element of the return tensor (because you almost
+// always need this CPU-side right after this function anyway).
+static at::Tensor ExclusiveScanAndTotal(
+    const at::Tensor& inTensor,
+    int64_t* optTotal = nullptr) {
+  const auto inSize = inTensor.sizes()[0];
+  auto retTensor = at::zeros({inSize + 1}, at::kLong).to(inTensor.device());
+
+  using at::indexing::None;
+  using at::indexing::Slice;
+  auto rightShiftedView = retTensor.index({Slice(1, None)});
+
+  // Do an (inclusive-scan) cumulative sum in to the view that's
+  // shifted one element to the right...
+  at::cumsum_out(rightShiftedView, inTensor, 0, at::kLong);
+
+  if (optTotal) {
+    *optTotal = retTensor[inSize].cpu().item<int64_t>();
+  }
+
+  // ...so that the not-shifted tensor holds the exclusive-scan
+  return retTensor;
+}
+
 // Entrance for marching cubes cuda extension. Marching Cubes is an algorithm to
 // create triangle meshes from an implicit function (one of the form f(x, y, z)
 // = 0). It works by iteratively checking a grid of cubes superimposed over a
@@ -444,20 +482,18 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
   using at::indexing::Slice;
 
   auto d_voxelVerts =
-      at::zeros({numVoxels + 1}, at::TensorOptions().dtype(at::kInt))
+      at::zeros({numVoxels}, at::TensorOptions().dtype(at::kInt))
           .to(vol.device());
-  auto d_voxelVerts_ = d_voxelVerts.index({Slice(1, None)});
   auto d_voxelOccupied =
-      at::zeros({numVoxels + 1}, at::TensorOptions().dtype(at::kInt))
+      at::zeros({numVoxels}, at::TensorOptions().dtype(at::kInt))
           .to(vol.device());
-  auto d_voxelOccupied_ = d_voxelOccupied.index({Slice(1, None)});
 
   // Execute "ClassifyVoxelKernel" kernel to precompute
   // two arrays - d_voxelOccupied and d_voxelVertices to global memory,
   // which stores the occupancy state and number of voxel vertices per voxel.
   ClassifyVoxelKernel<<<grid, threads, 0, stream>>>(
-      d_voxelVerts_.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
-      d_voxelOccupied_.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
+      d_voxelVerts.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
+      d_voxelOccupied.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
       vol.packed_accessor32<float, 3, at::RestrictPtrTraits>(),
       isolevel);
   AT_CUDA_CHECK(cudaGetLastError());
@@ -467,12 +503,9 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
   // count for voxels in the grid and compute the number of active voxels.
   // If the number of active voxels is 0, return zero tensor for verts and
   // faces.
-
-  auto d_voxelOccupiedScan = at::cumsum(d_voxelOccupied, 0);
-  auto d_voxelOccupiedScan_ = d_voxelOccupiedScan.index({Slice(1, None)});
-
-  // number of active voxels
-  int64_t activeVoxels = d_voxelOccupiedScan[numVoxels].cpu().item<int64_t>();
+  int64_t activeVoxels = 0;
+  auto d_voxelOccupiedScan =
+      ExclusiveScanAndTotal(d_voxelOccupied, &activeVoxels);
 
   const int device_id = vol.device().index();
   auto opt = at::TensorOptions().dtype(at::kInt).device(at::kCUDA, device_id);
@@ -487,24 +520,21 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
     return std::make_tuple(verts, faces, ids);
   }
 
-  // Execute "CompactVoxelsKernel" kernel to compress voxels for accleration.
+  // Execute "CompactVoxelsKernel" kernel to compress voxels for acceleration.
   // This allows us to run triangle generation on only the occupied voxels.
   auto d_compVoxelArray = at::zeros({activeVoxels}, opt);
   CompactVoxelsKernel<<<grid, threads, 0, stream>>>(
       d_compVoxelArray.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
       d_voxelOccupied.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
-      d_voxelOccupiedScan_
+      d_voxelOccupiedScan
           .packed_accessor32<int64_t, 1, at::RestrictPtrTraits>(),
       numVoxels);
   AT_CUDA_CHECK(cudaGetLastError());
   cudaDeviceSynchronize();
 
   // Scan d_voxelVerts array to generate offsets of vertices for each voxel
-  auto d_voxelVertsScan = at::cumsum(d_voxelVerts, 0);
-  auto d_voxelVertsScan_ = d_voxelVertsScan.index({Slice(1, None)});
-
-  // total number of vertices
-  int64_t totalVerts = d_voxelVertsScan[numVoxels].cpu().item<int64_t>();
+  int64_t totalVerts = 0;
+  auto d_voxelVertsScan = ExclusiveScanAndTotal(d_voxelVerts, &totalVerts);
 
   // Execute "GenerateFacesKernel" kernel
   // This runs only on the occupied voxels.
@@ -524,7 +554,7 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
       faces.packed_accessor<int64_t, 2, at::RestrictPtrTraits>(),
       ids.packed_accessor<int64_t, 1, at::RestrictPtrTraits>(),
       d_compVoxelArray.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
-      d_voxelVertsScan_.packed_accessor32<int64_t, 1, at::RestrictPtrTraits>(),
+      d_voxelVertsScan.packed_accessor32<int64_t, 1, at::RestrictPtrTraits>(),
       activeVoxels,
       vol.packed_accessor32<float, 3, at::RestrictPtrTraits>(),
       faceTable.packed_accessor32<int, 2, at::RestrictPtrTraits>(),