Evaluation
===================================
.. table:: Performance on `Mip-NeRF 360 Captures `_ (Averaged Over 7 Scenes)
+------------+-------+-------+-------+------------------+------------+
| | PSNR | SSIM | LPIPS | Train Mem | Train Time |
+============+=======+=======+=======+==================+============+
| inria-7k | 27.23 | 0.829 | 0.204 | 7.7 GB | 4m05s |
+------------+-------+-------+-------+------------------+------------+
| gsplat-7k | 27.21 | 0.831 | 0.202 | **4.3GB** | **5m35s** |
+------------+-------+-------+-------+------------------+------------+
| inria-30k | 28.95 | 0.870 | 0.138 | 9.0 GB | 37m13s |
+------------+-------+-------+-------+------------------+------------+
| gsplat-30k | 28.95 | 0.870 | 0.135 | **5.7 GB** | **35m49s** |
+------------+-------+-------+-------+------------------+------------+
This repo comes with a standalone script (:code:`examples/simple_trainer.py`) that reproduces
the `Gaussian Splatting `_ with
exactly the same performance on PSNR, SSIM, LPIPS, and converged number of Gaussians.
Powered by `gsplat`'s efficient CUDA implementation, the training takes up to
**4x less GPU memory** with up to **15% less time** to finish than the official implementation.
Trains Faster with Less GPU Memory
----------------------------------------------
+-----------------+---------+--------+---------+--------+---------+--------+--------+
| Train Mem (GB) | Bicycle | Bonsai | Counter | Garden | Kitchen | Room | Stump |
+=================+=========+========+=========+========+=========+========+========+
| inria-7k | 7.86 | 7.61 | 6.47 | 8.99 | 8.08 | 7.88 | 7.23 |
+-----------------+---------+--------+---------+--------+---------+--------+--------+
| gsplat-7k | **6.10**|**2.20**|**1.93** |**7.57**|**2.89** |**2.04**|**6.25**|
+-----------------+---------+--------+---------+--------+---------+--------+--------+
| inria-30k | 11.56 | 7.70 | 6.73 | 11.04 | 8.33 | 8.50 | 8.82 |
+-----------------+---------+--------+---------+--------+---------+--------+--------+
| gsplat-30k |**10.58**|**2.29**| **2.23**|**9.88**| **3.17**|**2.79**|**8.10**|
+-----------------+---------+--------+---------+--------+---------+--------+--------+
+-----------------+---------+--------+---------+--------+---------+--------+--------+
| Train Time (s) | Bicycle | Bonsai | Counter | Garden | Kitchen | Room | Stump |
+=================+=========+========+=========+========+=========+========+========+
| inria-7k | 336 | 340 | 364 | 427 | 436 | 336 | 321 |
+-----------------+---------+--------+---------+--------+---------+--------+--------+
| gsplat-7k | **319**| **299**| **318**| **415**| **389**|**301** |**304** |
+-----------------+---------+--------+---------+--------+---------+--------+--------+
| inria-30k | 2980 | 1552 | 1725 | 3092 | 2144 | 1773 | 2366 |
+-----------------+---------+--------+---------+--------+---------+--------+--------+
| gsplat-30k | **2964**|**1422**| **1621**|**3013**|**2020** |**1708**|**2299**|
+-----------------+---------+--------+---------+--------+---------+--------+--------+
Reproduced Metrics
-------------------
+------------+---------+--------+---------+--------+---------+-------+-------+
| PSNR | Bicycle | Bonsai | Counter | Garden | Kitchen | Room | Stump |
+============+=========+========+=========+========+=========+=======+=======+
| inria-7k | 23.59 | 29.75 | 27.21 | 26.13 | 29.02 | 29.26 | 25.64 |
+------------+---------+--------+---------+--------+---------+-------+-------+
| gsplat-7k | 23.71 | 29.66 | 27.14 | 26.30 | 28.86 | 29.21 | 25.62 |
+------------+---------+--------+---------+--------+---------+-------+-------+
| inria-30k | 25.19 | 32.21 | 29.02 | 27.29 | 31.07 | 31.31 | 26.56 |
+------------+---------+--------+---------+--------+---------+-------+-------+
| gsplat-30k | 25.22 | 32.06 | 29.02 | 27.32 | 31.16 | 31.36 | 26.53 |
+------------+---------+--------+---------+--------+---------+-------+-------+
+------------+---------+--------+---------+--------+---------+-------+-------+
| SSIM | Bicycle | Bonsai | Counter | Garden | Kitchen | Room | Stump |
+============+=========+========+=========+========+=========+=======+=======+
| inria-7k | 0.662 | 0.921 | 0.877 | 0.824 | 0.902 | 0.893 | 0.721 |
+------------+---------+--------+---------+--------+---------+-------+-------+
| gsplat-7k | 0.668 | 0.922 | 0.878 | 0.833 | 0.902 | 0.893 | 0.720 |
+------------+---------+--------+---------+--------+---------+-------+-------+
| inria-30k | 0.763 | 0.941 | 0.906 | 0.863 | 0.925 | 0.918 | 0.771 |
+------------+---------+--------+---------+--------+---------+-------+-------+
| gsplat-30k | 0.764 | 0.941 | 0.907 | 0.865 | 0.926 | 0.918 | 0.768 |
+------------+---------+--------+---------+--------+---------+-------+-------+
+------------+---------+--------+---------+--------+---------+-------+-------+
| LPIPS | Bicycle | Bonsai | Counter | Garden | Kitchen | Room | Stump |
+============+=========+========+=========+========+=========+=======+=======+
| inria-7k | 0.329 | 0.164 | 0.207 | 0.130 | 0.125 | 0.219 | 0.254 |
+------------+---------+--------+---------+--------+---------+-------+-------+
| gsplat-7k | 0.324 | 0.162 | 0.206 | 0.123 | 0.127 | 0.217 | 0.253 |
+------------+---------+--------+---------+--------+---------+-------+-------+
| inria-30k | 0.177 | 0.133 | 0.157 | 0.078 | 0.096 | 0.168 | 0.155 |
+------------+---------+--------+---------+--------+---------+-------+-------+
| gsplat-30k | 0.172 | 0.132 | 0.154 | 0.075 | 0.094 | 0.164 | 0.153 |
+------------+---------+--------+---------+--------+---------+-------+-------+
+-----------------+---------+--------+---------+--------+---------+-------+-------+
| Number of GSs | Bicycle | Bonsai | Counter | Garden | Kitchen | Room | Stump |
+=================+=========+========+=========+========+=========+=======+=======+
| inria-7k | 3.57M | 1.16M | 1.01M | 4.33M | 1.63M | 1.11M | 3.75M |
+-----------------+---------+--------+---------+--------+---------+-------+-------+
| gsplat-7k | 3.62M | 1.17M | 1.02M | 4.48M | 1.63M | 1.11M | 3.71M |
+-----------------+---------+--------+---------+--------+---------+-------+-------+
| inria-30k | 6.06M | 1.24M | 1.19M | 5.71M | 1.78M | 1.55M | 4.82M |
+-----------------+---------+--------+---------+--------+---------+-------+-------+
| gsplat-30k | 6.26M | 1.25M | 1.21M | 5.84M | 1.79M | 1.59M | 4.81M |
+-----------------+---------+--------+---------+--------+---------+-------+-------+
Note: Evaluations are conducted on a NVIDIA TITAN RTX GPU. The LPIPS metric is evaluated
using :code:`from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity`, which
is different from what's reported in the original paper that uses
:code:`from lpipsPyTorch import lpips`.
The evaluation of `gsplat-X` can be reproduced with the command
:code:`cd examples; bash benchmark.sh`
within the gsplat repo (commit 6acdce4).
The evaluation of `inria-X` can be
reproduced with our forked wersion of the official implementation at
`here `_,
with the command :code:`python full_eval_m360.py` (commit 36546ce).