Fix typos (#27)

nerfacc/occupancy_field.py

@@ -48,7 +48,7 @@ class OccupancyField(nn.Module):
         aabb: Scene bounding box. If ``num_dim=2`` it should be {min_x, min_y,max_x, max_y}.
             If ``num_dim=3`` it should be {min_x, min_y, min_z, max_x, max_y, max_z}.
         resolution: The field resolution. It can either be a int of a list of ints
-            to specify resolution on each dimention.  If ``num_dim=2`` it is for {res_x, res_y}.
+            to specify resolution on each dimension.  If ``num_dim=2`` it is for {res_x, res_y}.
             If ``num_dim=3`` it is for {res_x, res_y, res_z}. Default is 128.
         num_dim: The space dimension. Either 2 or 3. Default is 3.
 
@@ -216,7 +216,7 @@ class OccupancyField(nn.Module):
             occ_thre: Threshold to binarize the occupancy field.
             ema_decay: The decay rate for EMA updates.
             warmup_steps: Sample all cells during the warmup stage. After the warmup
-                stage we change the sampling strategy to 1/4 unifromly sampled cells
+                stage we change the sampling strategy to 1/4 uniformly sampled cells
                 together with 1/4 occupied cells.
             n: Update the field every n steps.
 

nerfacc/utils.py

@@ -71,7 +71,7 @@ def volumetric_marching(
     Returns:
         A tuple of tensors containing
 
-            - **packed_info**: Stores infomation on which samples belong to the same ray. \
+            - **packed_info**: Stores information on which samples belong to the same ray. \
                 It is a tensor with shape (n_rays, 2). For each ray, the two values \
                 indicate the start index and the number of samples for this ray, \
                 respectively.
@@ -180,7 +180,7 @@ def volumetric_rendering_weights(
     Note: this function is only differentiable to `sigmas`.
 
     Args:
-        packed_info: Stores infomation on which samples belong to the same ray. \
+        packed_info: Stores information on which samples belong to the same ray. \
             See ``volumetric_marching`` for details. Tensor with shape (n_rays, 2).
         sigmas: Densities at those samples. Tensor with shape (n_samples, 1).
         frustum_starts: Where the frustum-shape sample starts along a ray. Tensor with \
@@ -265,7 +265,7 @@ def unpack_to_ray_indices(packed_info: Tensor) -> Tensor:
     Note: this function is not differentiable to inputs.
 
     Args:
-        packed_info: Stores infomation on which samples belong to the same ray. \
+        packed_info: Stores information on which samples belong to the same ray. \
             See ``volumetric_marching`` for details. Tensor with shape (n_rays, 2).
 
     Returns:

nerfacc/volumetric_rendering.py

@@ -29,7 +29,7 @@ def volumetric_rendering_pipeline(
     This function is the integration of those individual functions:
 
     - ray_aabb_intersect
-    - volumetric_marhcing
+    - volumetric_marching
     - volumetric_rendering_steps
     - volumetric_rendering_weights
     - volumetric_rendering_accumulate