Docs (#37)

* update doc and bump version

* update docs with final scores

* minor fix

docs/source/examples/dnerf.rst

 Dynamic Scene
 ====================
 
+See code `examples/train_mlp_dnerf.py` at our `github repository`_ for details.
+
+Benchmarks
+------------
+
 Here we trained a 8-layer-MLP for the radiance field and a 4-layer-MLP for the warping field,
 (similar to the T-Nerf model in the `D-Nerf`_ paper) on the `D-Nerf dataset`_. We used train 
 split for training and test split for evaluation. Our experiments are conducted on a 
 single NVIDIA TITAN RTX GPU. 
 
-Note:
+.. note::
+
     The :ref:`Occupancy Grid` used in this example is shared by all the frames. In other words, 
     instead of using it to indicate the opacity of an area at a single timestamp, 
     Here we use it to indicate the `maximum` opacity at this area `over all the timestamps`.
@@ -25,3 +31,5 @@ Note:
 
 .. _`D-Nerf`: https://arxiv.org/abs/2104.00677
 .. _`D-Nerf dataset`: https://www.dropbox.com/s/0bf6fl0ye2vz3vr/data.zip?dl=0
+.. _`github repository`: https://github.com/KAIR-BAIR/nerfacc/
+

docs/source/examples/unbounded.rst

 Unbounded Scene
 ====================
 
+See code `examples/train_ngp_nerf.py` at our `github repository`_ for details.
+
+Benchmarks
+------------
+
 Here we trained a `Instant-NGP Nerf`_  on the `MipNerf360`_ dataset. We used train 
 split for training and test split for evaluation. Our experiments are conducted on a 
 single NVIDIA TITAN RTX GPU. 
@@ -34,3 +39,4 @@ that takes from `MipNerf360`_.
 .. _`Instant-NGP Nerf`: https://arxiv.org/abs/2103.13497
 .. _`MipNerf360`: https://arxiv.org/abs/2111.12077
 .. _`Nerf++`: https://arxiv.org/abs/2010.07492
+.. _`github repository`: https://github.com/KAIR-BAIR/nerfacc/