ngp.rst

.. _`Instant-NGP Example`:

Instant-NGP
====================

See code `examples/train_ngp_nerf.py` at our `github repository`_ for details.

Benchmarks
------------
*updated on 2023-03-14*

Here we trained a `Instant-NGP Nerf`_ model on the `Nerf-Synthetic dataset`_. We follow the same
settings with the Instant-NGP paper, which uses train split for training and test split for
evaluation. All experiments are conducted on a single NVIDIA TITAN RTX GPU. The training
memory footprint is about 3GB.

.. note::
    
    The Instant-NGP paper makes use of the alpha channel in the images to apply random background
    augmentation during training. For fair comparision, we rerun their code with a constant white
    background during both training and testing. Also it is worth to mention that we didn't strictly
    follow the training receipe in the Instant-NGP paper, such as the learning rate schedule etc, as
    the purpose of this benchmark is to showcase instead of reproducing the paper.

+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+
| PSNR                  | Lego  | Mic   |Materials| Chair |Hotdog | Ficus | Drums | Ship  | MEAN  |
|                       |       |       |         |       |       |       |       |       |       |
+=======================+=======+=======+=========+=======+=======+=======+=======+=======+=======+
|Instant-NGP 35k steps  | 35.87 | 36.22 | 29.08   | 35.10 | 37.48 | 30.61 | 23.85 | 30.62 | 32.35 |
+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+
|(training time)        | 309s  | 258s  | 256s    | 316s  | 292s  | 207s  | 218s  | 250s  | 263s  |
+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+
|Ours (occ) 20k steps   | 35.81 | 36.87 | 29.59   | 35.70 | 37.45 | 33.63 | 24.98 | 30.64 | 33.08 |
+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+
|(training time)        | 288s  | 255s  | 247s    | 319s  | 274s  | 238s  | 247s  | 252s  | 265s  |
+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+
|Ours (prop) 20k steps  | 34.06 | 34.32 | 27.93   | 34.27 | 36.47 | 31.39 | 24.39 | 30.57 | 31.68 |
+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+
|(training time)        | 238s  | 236s  | 250s    | 235s  | 235s  | 236s  | 236s  | 236s  | 240s  |
+-----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+

.. _`Instant-NGP Nerf`: https://github.com/NVlabs/instant-ngp/tree/51e4107edf48338e9ab0316d56a222e0adf87143
.. _`github repository`: https://github.com/KAIR-BAIR/nerfacc/
.. _`Nerf-Synthetic dataset`: https://drive.google.com/drive/folders/1JDdLGDruGNXWnM1eqY1FNL9PlStjaKWi