nerf.rst

.. _`Vanilla NeRF Example`:

Vanilla NeRF
====================
See code `examples/train_mlp_nerf.py` at our `github repository`_ for details.


Radiance Field
--------------
We follow the original `NeRF`_ paper to implement a 8-layer-MLP radiance field (`examples/radiance_fields/mlp.py`)
with positional encoding. 

.. note:: 
    The vanilla Nerf paper uses two MLPs for course-to-fine sampling. Instead here we only use a 
    single MLP with more samples (1024). Both ways share the same spirit to do dense sampling 
    around the surface. Our fast rendering inheritly skip samples away from the surface 
    so we can simplly increase the number of samples with a single MLP, to achieve the same goal 
    with the coarse-to-fine sampling, without runtime or memory issue.


Benchmark: Nerf-Synthetic Dataset
---------------------------------
*updated on 2022-10-08*

Our experiments are conducted on a single NVIDIA TITAN RTX GPU. 
The training memory footprint is about 10GB.

+----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+
| PSNR                 | Lego  | Mic   |Materials| Chair |Hotdog | Ficus | Drums | Ship  | MEAN  |
|                      |       |       |         |       |       |       |       |       |       |
+======================+=======+=======+=========+=======+=======+=======+=======+=======+=======+
| NeRF  (~ days)       | 32.54 | 32.91 | 29.62   | 33.00 | 36.18 | 30.13 | 25.01 | 28.65 | 31.00 |
+----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+
| Ours  (~ 1 hr)       | 33.69 | 33.76 | 29.73   | 33.32 | 35.80 | 32.52 | 25.39 | 28.18 | 31.55 |
+----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+

.. _`github repository`: https://github.com/KAIR-BAIR/nerfacc/
.. _`NeRF`: https://arxiv.org/abs/2003.08934