diff --git a/.gitmodules b/.gitmodules
index d20bef2..dc4f1bc 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -3,7 +3,7 @@
 	url = https://gitlab.inria.fr/bkerbl/simple-knn.git
 [submodule "submodules/diff-gaussian-rasterization"]
 	path = submodules/diff-gaussian-rasterization
-	url = https://github.com/graphdeco-inria/diff-gaussian-rasterization
+	url = https://github.com/jkulhanek/fork-diff-gaussian-rasterization.git
 [submodule "SIBR_viewers"]
 	path = SIBR_viewers
 	url = https://gitlab.inria.fr/sibr/sibr_core.git
diff --git a/gaussian_renderer/__init__.py b/gaussian_renderer/__init__.py
index f74e336..586b2a9 100644
--- a/gaussian_renderer/__init__.py
+++ b/gaussian_renderer/__init__.py
@@ -15,7 +15,7 @@ from diff_gaussian_rasterization import GaussianRasterizationSettings, GaussianR
 from scene.gaussian_model import GaussianModel
 from utils.sh_utils import eval_sh
 
-def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor, scaling_modifier = 1.0, override_color = None):
+def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor, scaling_modifier = 1.0, override_color = None, return_accumulation=False):
     """
     Render the scene. 
     
@@ -32,7 +32,6 @@ def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor,
     # Set up rasterization configuration
     tanfovx = math.tan(viewpoint_camera.FoVx * 0.5)
     tanfovy = math.tan(viewpoint_camera.FoVy * 0.5)
-
     raster_settings = GaussianRasterizationSettings(
         image_height=int(viewpoint_camera.image_height),
         image_width=int(viewpoint_camera.image_width),
@@ -45,7 +44,8 @@ def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor,
         sh_degree=pc.active_sh_degree,
         campos=viewpoint_camera.camera_center,
         prefiltered=False,
-        debug=pipe.debug
+        debug=pipe.debug,
+        return_accumulation=return_accumulation
     )
 
     rasterizer = GaussianRasterizer(raster_settings=raster_settings)
@@ -82,7 +82,7 @@ def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor,
         colors_precomp = override_color
 
     # Rasterize visible Gaussians to image, obtain their radii (on screen). 
-    rendered_image, radii = rasterizer(
+    rendered_image, radii, accumulation = rasterizer(
         means3D = means3D,
         means2D = means2D,
         shs = shs,
@@ -94,7 +94,10 @@ def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor,
 
     # Those Gaussians that were frustum culled or had a radius of 0 were not visible.
     # They will be excluded from value updates used in the splitting criteria.
-    return {"render": rendered_image,
-            "viewspace_points": screenspace_points,
-            "visibility_filter" : radii > 0,
-            "radii": radii}
+    out = {"render": rendered_image,
+           "viewspace_points": screenspace_points,
+           "visibility_filter" : radii > 0,
+           "radii": radii}
+    if raster_settings.return_accumulation:
+        out["accumulation"] = accumulation
+    return out
diff --git a/submodules/diff-gaussian-rasterization b/submodules/diff-gaussian-rasterization
index 59f5f77..6cf71af 160000
--- a/submodules/diff-gaussian-rasterization
+++ b/submodules/diff-gaussian-rasterization
@@ -1 +1 @@
-Subproject commit 59f5f77e3ddbac3ed9db93ec2cfe99ed6c5d121d
+Subproject commit 6cf71af574a8ac3463b7fcf02dfb54ecf4b684e8
diff --git a/train.py b/train.py
index 36faf0d..46f8a07 100644
--- a/train.py
+++ b/train.py
@@ -56,7 +56,9 @@ def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoi
                 net_image_bytes = None
                 custom_cam, do_training, pipe.convert_SHs_python, pipe.compute_cov3D_python, keep_alive, scaling_modifer = network_gui.receive()
                 if custom_cam != None:
-                    net_image = render(custom_cam, gaussians, pipe, background, scaling_modifer)["render"]
+                    out = render(custom_cam, gaussians, pipe, background, scaling_modifer, return_accumulation=True)
+                    # net_image = out["render"]
+                    net_image = out["accumulation"][None].repeat(3, 1, 1)
                     net_image_bytes = memoryview((torch.clamp(net_image, min=0, max=1.0) * 255).byte().permute(1, 2, 0).contiguous().cpu().numpy())
                 network_gui.send(net_image_bytes, dataset.source_path)
                 if do_training and ((iteration < int(opt.iterations)) or not keep_alive):