sync pref (#60)

nerfstudio-project · Oct 9, 2022 · 127223b · 127223b
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diff --git a/README.md b/README.md
@@ -14,7 +14,7 @@ Using NerfAcc,
 - The `Instant-NGP NeRF` model can be trained to *better quality* (+~0.7 PSNR) with *9/10th* of
   the training time (4.5 minutes) comparing to the official pure-CUDA implementation.
 - The `D-NeRF` model for *dynamic* objects can also be trained in *1 hour*
-  rather than *2 days* as in the paper, and with *better quality* (+~0.5 PSNR).
+  rather than *2 days* as in the paper, and with *better quality* (+~2.0 PSNR).
 - Both *bounded* and *unbounded* scenes are supported.
 
 **And it is pure Python interface with flexible APIs!**

diff --git a/docs/source/examples/dnerf.rst b/docs/source/examples/dnerf.rst
@@ -5,6 +5,7 @@ See code `examples/train_mlp_dnerf.py` at our `github repository`_ for details.
 
 Benchmarks
 ------------
+*updated on 2022-10-08*
 
 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 
@@ -24,12 +25,12 @@ single NVIDIA TITAN RTX GPU. The training memory footprint is about 11GB.
 +======================+==========+=========+=======+=========+=======+========+=========+=======+=======+
 | D-Nerf (~ days)      | 38.93    | 25.02   | 29.25 | 32.80   | 21.64 | 31.29  | 32.79   | 31.75 | 30.43 |
 +----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+
-| Ours  (~ 50min)      | 39.60    | 22.41   | 30.64 | 29.79   | 24.75 | 35.20  | 34.50   | 31.83 | 31.09 |
+| Ours  (~ 1 hr)       | 39.49    | 25.58   | 31.86 | 32.73   | 24.32 | 35.55  | 35.90   | 32.33 | 32.22 |
 +----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+
-| Ours  (Training time)| 45min    | 49min   | 51min | 46min   | 53min | 57min  | 49min   | 46min | 50min |
+| Ours  (Training time)| 37min    | 52min   | 69min | 64min   | 44min | 79min  | 79min   | 39min | 58min |
 +----------------------+----------+---------+-------+---------+-------+--------+---------+-------+-------+
 
 .. _`D-Nerf`: https://arxiv.org/abs/2011.13961
 .. _`D-Nerf dataset`: https://www.dropbox.com/s/0bf6fl0ye2vz3vr/data.zip?dl=0
-.. _`github repository`: https://github.com/KAIR-BAIR/nerfacc/
+.. _`github repository`: https://github.com/KAIR-BAIR/nerfacc/tree/5637cc9a1565b2685c02250eb1ee1c53d3b07af1
 
diff --git a/docs/source/examples/ngp.rst b/docs/source/examples/ngp.rst
@@ -31,5 +31,5 @@ memory footprint is about 3GB.
 +----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+
 
 .. _`Instant-NGP Nerf`: https://arxiv.org/abs/2201.05989
-.. _`github repository`: https://github.com/KAIR-BAIR/nerfacc/
+.. _`github repository`: https://github.com/KAIR-BAIR/nerfacc/tree/5637cc9a1565b2685c02250eb1ee1c53d3b07af1
 .. _`Nerf-Synthetic dataset`: https://drive.google.com/drive/folders/1JDdLGDruGNXWnM1eqY1FNL9PlStjaKWi
diff --git a/docs/source/examples/unbounded.rst b/docs/source/examples/unbounded.rst
@@ -40,4 +40,4 @@ that takes from `MipNerf360`_.
 .. _`Instant-NGP Nerf`: https://arxiv.org/abs/2201.05989
 .. _`MipNerf360`: https://arxiv.org/abs/2111.12077
 .. _`Nerf++`: https://arxiv.org/abs/2010.07492
-.. _`github repository`: https://github.com/KAIR-BAIR/nerfacc/
+.. _`github repository`: https://github.com/KAIR-BAIR/nerfacc/tree/5637cc9a1565b2685c02250eb1ee1c53d3b07af1
diff --git a/docs/source/examples/vanilla.rst b/docs/source/examples/vanilla.rst
@@ -5,6 +5,7 @@ See code `examples/train_mlp_nerf.py` at our `github repository`_ for details.
 
 Benchmarks
 ------------
+*updated on 2022-10-08*
 
 Here we trained a 8-layer-MLP for the radiance field as in the `vanilla Nerf`_. We used the 
 train split for training and test split for evaluation as in the Nerf paper. Our experiments are 
@@ -28,5 +29,5 @@ conducted on a single NVIDIA TITAN RTX GPU. The training memory footprint is abo
 | Ours  (Training time)| 58min | 53min | 46min   | 62min | 56min | 42min | 52min | 49min | 52min |
 +----------------------+-------+-------+---------+-------+-------+-------+-------+-------+-------+
 
-.. _`github repository`: : https://github.com/KAIR-BAIR/nerfacc/
+.. _`github repository`: : https://github.com/KAIR-BAIR/nerfacc/tree/5637cc9a1565b2685c02250eb1ee1c53d3b07af1
 .. _`vanilla Nerf`: https://arxiv.org/abs/2003.08934
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -11,7 +11,7 @@ Using NerfAcc,
 - The `Instant-NGP Nerf`_ model can be trained to *better quality* (+~0.7 PSNR) with *9/10th* of \
   the training time (4.5 minutes) comparing to the official pure-CUDA implementation.
 - The `D-Nerf`_ model for *dynamic* objects can also be trained in *1 hour* \
-  rather than *2 days* as in the paper, and with *better quality* (+~0.5 PSNR).
+  rather than *2 days* as in the paper, and with *better quality* (+~2.0 PSNR).
 - Both *bounded* and *unbounded* scenes are supported.
 
 **And it is pure Python interface with flexible APIs!**