example of use

.readthedocs.yaml

+version: 2
+
+python:
+  version: 3.9
+  install:
+    - requirements: requirements.txt
+    - requirements: docs/requirements.txt
+    - method: pip
+      path: .
+
+sphinx:
+  fail_on_warning: true
\ No newline at end of file

docs/source/index.rst

-Welcome to nerfacc's documentation!
+Welcome to the nerfacc documentation!
 ===================================
 
-**Lumache** (/lu'make/) is a Python library for cooks and food lovers
-that creates recipes mixing random ingredients.
-It pulls data from the `Open Food Facts database <https://world.openfoodfacts.org/>`_
-and offers a *simple* and *intuitive* API.
+This is a fast differentiable volume rendering toolbox as a PyTorch extension for NeRF.
 
 Check out the :doc:`usage` section for further information, including
 how to :ref:`installation` the project.
@@ -18,5 +15,4 @@ Contents
 
 .. toctree::
 
-   usage
-   api
\ No newline at end of file
+   usage
\ No newline at end of file

docs/source/usage.rst

+Usage
+=====
+
+.. _installation:
+
+Installation
+------------
+
+To use nerfacc, first install it using pip:
+
+.. code-block:: console
+
+   (.venv) $ pip install git+https://github.com/liruilong940607/nerfacc
+
+Example of use
+----------------
+
+.. code-block:: python
+
+    from typing import Callable, List, Union
+    from torch import Tensor
+    import torch
+    import torch.nn.function as F
+
+    from nerfacc import OccupancyField, volumetric_rendering
+
+    # setup the scene bounding box.
+    scene_aabb = torch.tensor([-1.5, -1.5, -1.5, 1.5, 1.5, 1.5]).cuda()
+
+    # setup the scene radiance field. Assume you have a NeRF model and 
+    # it has following functions:
+    # - query_density(): {x} -> {density} 
+    # - forward(): {x, dirs} -> {rgb, density}
+    radiance_field = ...
+
+    # setup some rendering settings
+    render_n_samples = 1024
+    render_bkgd = torch.ones(3).cuda()
+    render_step_size = (
+        (scene_aabb[3:] - scene_aabb[:3]).max() * math.sqrt(3) / render_n_samples
+    )
+    
+    # setup occupancy field with eval function
+    def occ_eval_fn(x: torch.Tensor) -> torch.Tensor:
+        """Evaluate occupancy given positions.
+
+        Args:
+            x: positions with shape (N, 3).
+        Returns:
+            occupancy values with shape (N, 1). 
+        """
+        density_after_activation = radiance_field.query_density(x)
+        occupancy = density_after_activation * render_step_size
+        return occupancy
+    occ_field = OccupancyField(occ_eval_fn=occ_eval_fn, aabb=aabb, resolution=128)
+
+    # training
+    for step in range(10_000):
+        # generate rays from data and the gt pixel color
+        rays = ...
+        pixels = ...
+
+        # update occupancy grid
+        occ_field.every_n_step(step)        
+
+        # rendering
+        (
+            accumulated_color,
+            accumulated_depth,
+            accumulated_weight,
+            _,
+        ) = volumetric_rendering(
+            query_fn=radiance_field.forward,  # {x, dir} -> {rgb, density}
+            rays_o=rays.origins,
+            rays_d=rays.viewdirs,
+            scene_aabb=aabb,
+            scene_occ_binary=occupancy_field.occ_grid_binary,
+            scene_resolution=occupancy_field.resolution,
+            render_bkgd=render_bkgd,
+            render_n_samples=render_n_samples,
+            # other kwargs for `query_fn`
+            ...,
+        )
+
+        # compute loss
+        loss = F.mse_loss(accumulated_color, pixels)