diff --git a/models/__init__.py b/models/__init__.py
index 8cbe384..8271e1f 100644
--- a/models/__init__.py
+++ b/models/__init__.py
@@ -104,6 +104,7 @@ def render(self, rays):
             rays: [..., 6] shaped tensor (3 for origin, 3 for direction)
         """
         rays_o, rays_d = rays[..., :3], rays[..., 3:] # origins, viewdirs
+
         z_vals = torch.linspace(self.near, self.far,
                                 self.n_samples_per_ray).unsqueeze(0).to(rays_o)
 
@@ -142,7 +143,7 @@ def render(self, rays):
                     self.main_net(pts[valid_rays])) * self.density_scale
 
             # alpha & weights
-            alpha = 1. - torch.exp(-sigma * dists)
+            alpha = 1. - torch.exp(-sigma * dists * self.density_scale)
             weights = alpha * torch.cumprod(F.pad(1 - alpha + 1e-10, [1, 0],
                                                   value=1),
                                             -1)[..., :-1] # exclusive cumprod
diff --git a/models/grid_based.py b/models/grid_based.py
index 3900ae3..ff787ce 100644
--- a/models/grid_based.py
+++ b/models/grid_based.py
@@ -168,7 +168,10 @@ def __init__(self, resolution: int, n_chan: int, out_dim=1):
             1e-1 * torch.randn(3, n_chan, 1, resolution),
             requires_grad=True)
 
-        self.basis_mat = nn.Linear(n_chan*3, out_dim, bias=False)
+        if out_dim > 1:
+            self.basis_mat = nn.Linear(n_chan*3, out_dim, bias=False)
+        else:
+            self.basis_mat = None
 
     def forward(self, coords, *args, **kwargs):
         # [B, 3] to [1, B, 1, 3]
@@ -188,14 +191,17 @@ def forward(self, coords, *args, **kwargs):
         feats1 = F.grid_sample(grid, F.pad(torch.cat([coords[..., (0,)],
                                                        coords[..., (1,)],
                                                        coords[..., (2,)]], 0),
-                                            (1, 0)),
+                                            (0, 1)),
                                 mode='bilinear',
                                 padding_mode='zeros', align_corners=True)
         feats1 = feats1.squeeze(-1).permute(2, 1, 0) # [B, C, 3]
 
         feats0 = (feats0 * feats1).flatten(1, -1) # [B, C*3]
         del feats1
-        return self.basis_mat(feats0).squeeze(-1)
+
+        if self.basis_mat is not None:
+            return self.basis_mat(feats0).squeeze(-1)
+        return feats0.sum(dim=-1)
 
     def compute_tv(self):
         return F.mse_loss(self.planes[..., 1:, :], self.planes[..., :-1, :]) \