update docs for ray gradients (#62)

README.md

@@ -39,9 +39,9 @@ from torch import Tensor
 import nerfacc 
 
 radiance_field = ...  # network: a NeRF model
-optimizer = ...  # network optimizer
 rays_o: Tensor = ...  # ray origins. (n_rays, 3)
 rays_d: Tensor = ...  # ray normalized directions. (n_rays, 3)
+optimizer = ...  # optimizer
 
 def sigma_fn(
     t_starts: Tensor, t_ends:Tensor, ray_indices: Tensor
@@ -76,16 +76,17 @@ def rgb_sigma_fn(
 
 # Efficient Raymarching: Skip empty and occluded space, pack samples from all rays.
 # packed_info: (n_rays, 2). t_starts: (n_samples, 1). t_ends: (n_samples, 1).
-packed_info, t_starts, t_ends = nerfacc.ray_marching(
-    rays_o, rays_d, sigma_fn=sigma_fn, near_plane=0.2, far_plane=1.0, 
-    early_stop_eps=1e-4, alpha_thre=1e-2, 
-)
+with torch.no_grad():
+    packed_info, t_starts, t_ends = nerfacc.ray_marching(
+        rays_o, rays_d, sigma_fn=sigma_fn, near_plane=0.2, far_plane=1.0, 
+        early_stop_eps=1e-4, alpha_thre=1e-2, 
+    )
 
 # Differentiable Volumetric Rendering.
 # colors: (n_rays, 3). opaicity: (n_rays, 1). depth: (n_rays, 1).
 color, opacity, depth = nerfacc.rendering(rgb_sigma_fn, packed_info, t_starts, t_ends)
 
-# Optimize the radience field.
+# Optimize: Both the network and rays will receive gradients
 optimizer.zero_grad()
 loss = F.mse_loss(color, color_gt)
 loss.backward()

docs/source/index.rst

@@ -53,9 +53,9 @@ An simple example is like this:
    import nerfacc 
 
    radiance_field = ...  # network: a NeRF model
-   optimizer = ...  # network optimizer
    rays_o: Tensor = ...  # ray origins. (n_rays, 3)
    rays_d: Tensor = ...  # ray normalized directions. (n_rays, 3)
+   optimizer = ...  # optimizer
 
    def sigma_fn(
       t_starts: Tensor, t_ends:Tensor, ray_indices: Tensor
@@ -90,16 +90,17 @@ An simple example is like this:
 
    # Efficient Raymarching: Skip empty and occluded space, pack samples from all rays.
    # packed_info: (n_rays, 2). t_starts: (n_samples, 1). t_ends: (n_samples, 1).
-   packed_info, t_starts, t_ends = nerfacc.ray_marching(
-      rays_o, rays_d, sigma_fn=sigma_fn, near_plane=0.2, far_plane=1.0, 
-      early_stop_eps=1e-4, alpha_thre=1e-2, 
-   )
+   with torch.no_grad():
+      packed_info, t_starts, t_ends = nerfacc.ray_marching(
+         rays_o, rays_d, sigma_fn=sigma_fn, near_plane=0.2, far_plane=1.0, 
+         early_stop_eps=1e-4, alpha_thre=1e-2, 
+      )
 
    # Differentiable Volumetric Rendering.
    # colors: (n_rays, 3). opaicity: (n_rays, 1). depth: (n_rays, 1).
    color, opacity, depth = nerfacc.rendering(rgb_sigma_fn, packed_info, t_starts, t_ends)
 
-   # Optimize the radience field.
+   # Optimize: Both the network and rays will receive gradients
    optimizer.zero_grad()
    loss = F.mse_loss(color, color_gt)
    loss.backward()