Update README.md

README.md

@@ -485,6 +485,16 @@ python convert.py -s <location> --skip_matching [--resize] #If not resizing, Ima
 
 - *I'm on Windows and I can't manage to build the submodules, what do I do?* Consider following the steps in the excellent video tutorial [here](https://www.youtube.com/watch?v=UXtuigy_wYc), hopefully they should help. The order in which the steps are done is important! Alternatively, consider using the linked Colab template.
 
+- *It still doesn't work. It says something about ```cl.exe```* User Henry Pearce found a workaround. You can you try adding the visual studio path to your environment variables (your version number might differ);
+```C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.30133\bin\Hostx64\x64```
+Then make sure you start a new conda prompt and cd to your repo location and try this;
+```
+conda activate gaussian_splatting
+cd <dir_to_repo>/gaussian-splatting
+pip install submodules\diff-gaussian-rasterization
+pip install submodules\simple-knn
+```
+
 - *I'm on macOS/Puppy Linux/Greenhat and I can't manage to build, what do I do?* Sorry, we can't provide support for platforms outside of the ones we list in this README. Consider using the linked Colab template.
 
 - *I don't have 24 GB of VRAM for training, what do I do?* The VRAM consumption is determined by the number of points that are being optimized, which increases over time. If you only want to train to 7k iterations, you will need significantly less. To do the full training routine and avoid running out of memory, you can increase the ```--densify_grad_threshold```, ```--densification_interval``` or reduce the value of ```--densify_until_iter```. Note however that this will affect the quality of the result. Also try setting ```--test_iterations``` to ```-1``` to avoid memory spikes during testing. If ```--densify_grad_threshold``` is very high, no densification should occur and training should complete if the scene itself loads successfully.