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https://github.com/TimSong412/2D-surfel-gaussian

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README 

        
# Implementation of 2D Gaussian Splatting for Geometrically Accurate Radiance Fields

Yunzhou Song (Tim), Zixuan Lin (Jack), Yexin Zhang



GRASP lab, University of Pennsylvania



This repository contains the unofficial implementation of the paper ["2D Gaussian Splatting for Geometrically Accurate Radiance Fields"](https://arxiv.org/pdf/2403.17888).

The detailed equations of our method are [here](https://cubic-keeper-3fa.notion.site/Release-Specification-7e01961c464040e583791b3654523127?pvs=4).



Our implementation is as perfect in photometric reconstruction as that of 3D gs. We are eager to receive suggestions and open to feedback and criticism!



We are still debugging!



240502: Fix negative ray_M gradient.



240505: Add edge-aware distortion.



240511: Add precedure regularization weight and different Ln



## Acknowledgements

This project is built upon [3DGS](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/).



## Overview



  

   





  

   



**Rendered** RGB Image (left), **Rendered** Normal (right)



## Cloning the Repository



The repository contains submodules, thus please check it out with 

```shell

# SSH

git clone [email protected]:TimSong412/2D-gaussian.git --recursive

```

or

```shell

# HTTPS

git clone https://github.com/TimSong412/2D-gaussian.git --recursive

```



## Optimizer



The optimizer uses PyTorch and CUDA extensions in a Python environment to produce trained models. 



### Hardware Requirements 



- CUDA-ready GPU with Compute Capability 8.6+

- 24 GB VRAM (to train to paper evaluation quality)

- Please see FAQ for smaller VRAM configurations



### Software Requirements 

- Conda (recommended for easy setup)

- C++ Compiler for PyTorch extensions 

- CUDA SDK 12 for PyTorch extensions, install *after* Visual Studio (we used 12.1)

- C++ Compiler and CUDA SDK must be compatible



### Setup



**Download datasets**

```shell

sh download.sh

```



**Set up environment**



You can run the following in parallel with the previous step



```shell

conda create -n gs python=3.8 

conda activate gs

pip install -r requirements.txt

pip install submodules/simple-knn

bash update_pkg.sh

```



**Configuration**



Checkout [Config.h](https://github.com/TimSong412/2D-surfel-gaussian/blob/main/submodules/diff-gaussian-rasterization/cuda_rasterizer/config.h) for options of regularization.



```c

#define Ld

```

and 

```c

#define Ln

```

turn on and off depth distortion (Ld) regularizer and normal consistency regularizer (Ln). 

The weight of `Ld` and `Ln` can be adjusted at (or around) line 163 of `train.py`. 



**Compile Rasterizer**



Build binary of cuda rasterier



```shell

bash update_pkg.sh

```



### Running



To run the optimizer, simply use



```shell

python train.py -s 

```



*example*

```shell

python train.py -s dataset/garden

```



Command Line Arguments for train.py



  #### --source_path / -s

  Path to the source directory containing a COLMAP or Synthetic NeRF data set.

  #### --model_path / -m 

  Path where the trained model should be stored (```output/``` by default).

  #### --images / -i

  Alternative subdirectory for COLMAP images (```images``` by default).

  #### --eval

  Add this flag to use a MipNeRF360-style training/test split for evaluation.

  #### --resolution / -r

  Specifies resolution of the loaded images before training. If provided ```1, 2, 4``` or ```8```, uses original, 1/2, 1/4 or 1/8 resolution, respectively. For all other values, rescales the width to the given number while maintaining image aspect. **If not set and input image width exceeds 1.6K pixels, inputs are automatically rescaled to this target.**

  #### --data_device

  Specifies where to put the source image data, ```cuda``` by default, recommended to use ```cpu``` if training on large/high-resolution dataset, will reduce VRAM consumption, but slightly slow down training. Thanks to [HrsPythonix](https://github.com/HrsPythonix).

  #### --white_background / -w

  Add this flag to use white background instead of black (default), e.g., for evaluation of NeRF Synthetic dataset.

  #### --sh_degree

  Order of spherical harmonics to be used (no larger than 3). ```3``` by default.

  #### --convert_SHs_python

  Flag to make pipeline compute forward and backward of SHs with PyTorch instead of ours.

  #### --convert_cov3D_python

  Flag to make pipeline compute forward and backward of the 3D covariance with PyTorch instead of ours.

  #### --debug

  Enables debug mode if you experience erros. If the rasterizer fails, a ```dump``` file is created that you may forward to us in an issue so we can take a look.

  #### --debug_from

  Debugging is **slow**. You may specify an iteration (starting from 0) after which the above debugging becomes active.

  #### --iterations

  Number of total iterations to train for, ```30_000``` by default.

  #### --ip

  IP to start GUI server on, ```127.0.0.1``` by default.

  #### --port 

  Port to use for GUI server, ```6009``` by default.

  #### --test_iterations

  Space-separated iterations at which the training script computes L1 and PSNR over test set, ```7000 30000``` by default.

  #### --save_iterations

  Space-separated iterations at which the training script saves the Gaussian model, ```7000 30000 ``` by default.

  #### --checkpoint_iterations

  Space-separated iterations at which to store a checkpoint for continuing later, saved in the model directory.

  #### --start_checkpoint

  Path to a saved checkpoint to continue training from.

  #### --quiet 

  Flag to omit any text written to standard out pipe. 

  #### --feature_lr

  Spherical harmonics features learning rate, ```0.0025``` by default.

  #### --opacity_lr

  Opacity learning rate, ```0.05``` by default.

  #### --scaling_lr

  Scaling learning rate, ```0.005``` by default.

  #### --rotation_lr

  Rotation learning rate, ```0.001``` by default.

  #### --position_lr_max_steps

  Number of steps (from 0) where position learning rate goes from ```initial``` to ```final```. ```30_000``` by default.

  #### --position_lr_init

  Initial 3D position learning rate, ```0.00016``` by default.

  #### --position_lr_final

  Final 3D position learning rate, ```0.0000016``` by default.

  #### --position_lr_delay_mult

  Position learning rate multiplier (cf. Plenoxels), ```0.01``` by default. 

  #### --densify_from_iter

  Iteration where densification starts, ```500``` by default. 

  #### --densify_until_iter

  Iteration where densification stops, ```15_000``` by default.

  #### --densify_grad_threshold

  Limit that decides if points should be densified based on 2D position gradient, ```0.0002``` by default.

  #### --densification_interval

  How frequently to densify, ```100``` (every 100 iterations) by default.

  #### --opacity_reset_interval

  How frequently to reset opacity, ```3_000``` by default. 

  #### --lambda_dssim

  Influence of SSIM on total loss from 0 to 1, ```0.2``` by default. 

  #### --percent_dense

  Percentage of scene extent (0--1) a point must exceed to be forcibly densified, ```0.01``` by default.







Note that similar to MipNeRF360, we target images at resolutions in the 1-1.6K pixel range. For convenience, arbitrary-size inputs can be passed and will be automatically resized if their width exceeds 1600 pixels. We recommend to keep this behavior, but you may force training to use your higher-resolution images by setting ```-r 1```.



The MipNeRF360 scenes are hosted by the paper authors [here](https://jonbarron.info/mipnerf360/). You can find our SfM data sets for Tanks&Temples and Deep Blending [here](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/input/tandt_db.zip). If you do not provide an output model directory (```-m```), trained models are written to folders with randomized unique names inside the ```output``` directory. At this point, the trained models may be viewed with the real-time viewer (see further below).



### Additional Visualization



In additional to RGB images, the script will render depth map, normal map, and 3D gaussian visualization



```shell

python vis_depth.py -m output/xxxxx --eval

```



### *-----The Rest Are Identitcal To That Of 3DGS-----*



### Evaluation

By default, the trained models use all available images in the dataset. To train them while withholding a test set for evaluation, use the ```--eval``` flag. This way, you can render training/test sets and produce error metrics as follows:

```shell

python train.py -s  --eval # Train with train/test split

python render.py -m  # Generate renderings

python metrics.py -m  # Compute error metrics on renderings

```



If you want to evaluate our [pre-trained models](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/pretrained/models.zip), you will have to download the corresponding source data sets and indicate their location to ```render.py``` with an additional ```--source_path/-s``` flag. Note: The pre-trained models were created with the release codebase. This code base has been cleaned up and includes bugfixes, hence the metrics you get from evaluating them will differ from those in the paper.

```shell

python render.py -m  -s 

python metrics.py -m 

```



Command Line Arguments for render.py



  #### --model_path / -m 

  Path to the trained model directory you want to create renderings for.

  #### --skip_train

  Flag to skip rendering the training set.

  #### --skip_test

  Flag to skip rendering the test set.

  #### --quiet 

  Flag to omit any text written to standard out pipe. 



  **The below parameters will be read automatically from the model path, based on what was used for training. However, you may override them by providing them explicitly on the command line.** 



  #### --source_path / -s

  Path to the source directory containing a COLMAP or Synthetic NeRF data set.

  #### --images / -i

  Alternative subdirectory for COLMAP images (```images``` by default).

  #### --eval

  Add this flag to use a MipNeRF360-style training/test split for evaluation.

  #### --resolution / -r

  Changes the resolution of the loaded images before training. If provided ```1, 2, 4``` or ```8```, uses original, 1/2, 1/4 or 1/8 resolution, respectively. For all other values, rescales the width to the given number while maintaining image aspect. ```1``` by default.

  #### --white_background / -w

  Add this flag to use white background instead of black (default), e.g., for evaluation of NeRF Synthetic dataset.

  #### --convert_SHs_python

  Flag to make pipeline render with computed SHs from PyTorch instead of ours.

  #### --convert_cov3D_python

  Flag to make pipeline render with computed 3D covariance from PyTorch instead of ours.



Command Line Arguments for metrics.py



  #### --model_paths / -m 

  Space-separated list of model paths for which metrics should be computed.







We further provide the ```full_eval.py``` script. This script specifies the routine used in our evaluation and demonstrates the use of some additional parameters, e.g., ```--images (-i)``` to define alternative image directories within COLMAP data sets. If you have downloaded and extracted all the training data, you can run it like this:

```shell

python full_eval.py -m360  -tat  -db 

```

In the current version, this process takes about 7h on our reference machine containing an A6000. If you want to do the full evaluation on our pre-trained models, you can specify their download location and skip training. 

```shell

python full_eval.py -o  --skip_training -m360  -tat  -db 

```



If you want to compute the metrics on our paper's [evaluation images](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/evaluation/images.zip), you can also skip rendering. In this case it is not necessary to provide the source datasets. You can compute metrics for multiple image sets at a time. 

```shell

python full_eval.py -m /garden ... --skip_training --skip_rendering

```



Command Line Arguments for full_eval.py

  

  #### --skip_training

  Flag to skip training stage.

  #### --skip_rendering

  Flag to skip rendering stage.

  #### --skip_metrics

  Flag to skip metrics calculation stage.

  #### --output_path

  Directory to put renderings and results in, ```./eval``` by default, set to pre-trained model location if evaluating them.

  #### --mipnerf360 / -m360

  Path to MipNeRF360 source datasets, required if training or rendering.

  #### --tanksandtemples / -tat

  Path to Tanks&Temples source datasets, required if training or rendering.

  #### --deepblending / -db

  Path to Deep Blending source datasets, required if training or rendering.







## Interactive Viewers

We provide two interactive viewers for our method: remote and real-time. Our viewing solutions are based on the [SIBR](https://sibr.gitlabpages.inria.fr/) framework, developed by the GRAPHDECO group for several novel-view synthesis projects.



### Hardware Requirements

- OpenGL 4.5-ready GPU and drivers (or latest MESA software)

- 4 GB VRAM recommended

- CUDA-ready GPU with Compute Capability 7.0+ (only for Real-Time Viewer)



### Software Requirements

- Visual Studio or g++, **not Clang** (we used Visual Studio 2019 for Windows)

- CUDA SDK 11, install *after* Visual Studio (we used 11.8)

- CMake (recent version, we used 3.24)

- 7zip (only on Windows)



### Pre-built Windows Binaries

We provide pre-built binaries for Windows [here](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/binaries/viewers.zip). We recommend using them on Windows for an efficient setup, since the building of SIBR involves several external dependencies that must be downloaded and compiled on-the-fly.



### Installation from Source

If you cloned with submodules (e.g., using ```--recursive```), the source code for the viewers is found in ```SIBR_viewers```. The network viewer runs within the SIBR framework for Image-based Rendering applications.



#### Windows

CMake should take care of your dependencies.

```shell

cd SIBR_viewers

cmake -Bbuild .

cmake --build build --target install --config RelWithDebInfo

```

You may specify a different configuration, e.g. ```Debug``` if you need more control during development.



#### Ubuntu 22.04

You will need to install a few dependencies before running the project setup.

```shell

# Dependencies

sudo apt install -y libglew-dev libassimp-dev libboost-all-dev libgtk-3-dev libopencv-dev libglfw3-dev libavdevice-dev libavcodec-dev libeigen3-dev libxxf86vm-dev libembree-dev

# Project setup

cd SIBR_viewers

cmake -Bbuild . -DCMAKE_BUILD_TYPE=Release # add -G Ninja to build faster

cmake --build build -j24 --target install

``` 



#### Ubuntu 20.04

Backwards compatibility with Focal Fossa is not fully tested, but building SIBR with CMake should still work after invoking

```shell

git checkout fossa_compatibility

```



### Navigation in SIBR Viewers

The SIBR interface provides several methods of navigating the scene. By default, you will be started with an FPS navigator, which you can control with ```W, A, S, D, Q, E``` for camera translation and ```I, K, J, L, U, O``` for rotation. Alternatively, you may want to use a Trackball-style navigator (select from the floating menu). You can also snap to a camera from the data set with the ```Snap to``` button or find the closest camera with ```Snap to closest```. The floating menues also allow you to change the navigation speed. You can use the ```Scaling Modifier``` to control the size of the displayed Gaussians, or show the initial point cloud.



### Running the Network Viewer



https://github.com/graphdeco-inria/gaussian-splatting/assets/40643808/90a2e4d3-cf2e-4633-b35f-bfe284e28ff7



After extracting or installing the viewers, you may run the compiled ```SIBR_remoteGaussian_app[_config]``` app in ```/bin```, e.g.: 

```shell

.//bin/SIBR_remoteGaussian_app

```

The network viewer allows you to connect to a running training process on the same or a different machine. If you are training on the same machine and OS, no command line parameters should be required: the optimizer communicates the location of the training data to the network viewer. By default, optimizer and network viewer will try to establish a connection on **localhost** on port **6009**. You can change this behavior by providing matching ```--ip``` and ```--port``` parameters to both the optimizer and the network viewer. If for some reason the path used by the optimizer to find the training data is not reachable by the network viewer (e.g., due to them running on different (virtual) machines), you may specify an override location to the viewer by using ```-s ```. 



Primary Command Line Arguments for Network Viewer



  #### --path / -s

  Argument to override model's path to source dataset.

  #### --ip

  IP to use for connection to a running training script.

  #### --port

  Port to use for connection to a running training script. 

  #### --rendering-size 

  Takes two space separated numbers to define the resolution at which network rendering occurs, ```1200``` width by default.

  Note that to enforce an aspect that differs from the input images, you need ```--force-aspect-ratio``` too.

  #### --load_images

  Flag to load source dataset images to be displayed in the top view for each camera.







### Running the Real-Time Viewer



https://github.com/graphdeco-inria/gaussian-splatting/assets/40643808/0940547f-1d82-4c2f-a616-44eabbf0f816



After extracting or installing the viewers, you may run the compiled ```SIBR_gaussianViewer_app[_config]``` app in ```/bin```, e.g.: 

```shell

.//bin/SIBR_gaussianViewer_app -m 

```



It should suffice to provide the ```-m``` parameter pointing to a trained model directory. Alternatively, you can specify an override location for training input data using ```-s```. To use a specific resolution other than the auto-chosen one, specify ```--rendering-size  ```. Combine it with ```--force-aspect-ratio``` if you want the exact resolution and don't mind image distortion. 



**To unlock the full frame rate, please disable V-Sync on your machine and also in the application (Menu → Display). In a multi-GPU system (e.g., laptop) your OpenGL/Display GPU should be the same as your CUDA GPU (e.g., by setting the application's GPU preference on Windows, see below) for maximum performance.**



![Teaser image](assets/select.png)



In addition to the initial point cloud and the splats, you also have the option to visualize the Gaussians by rendering them as ellipsoids from the floating menu.

SIBR has many other functionalities, please see the [documentation](https://sibr.gitlabpages.inria.fr/) for more details on the viewer, navigation options etc. There is also a Top View (available from the menu) that shows the placement of the input cameras and the original SfM point cloud; please note that Top View slows rendering when enabled. The real-time viewer also uses slightly more aggressive, fast culling, which can be toggled in the floating menu. If you ever encounter an issue that can be solved by turning fast culling off, please let us know.



Primary Command Line Arguments for Real-Time Viewer



  #### --model-path / -m

  Path to trained model.

  #### --iteration

  Specifies which of state to load if multiple are available. Defaults to latest available iteration.

  #### --path / -s

  Argument to override model's path to source dataset.

  #### --rendering-size 

  Takes two space separated numbers to define the resolution at which real-time rendering occurs, ```1200``` width by default. Note that to enforce an aspect that differs from the input images, you need ```--force-aspect-ratio``` too.

  #### --load_images

  Flag to load source dataset images to be displayed in the top view for each camera.

  #### --device

  Index of CUDA device to use for rasterization if multiple are available, ```0``` by default.

  #### --no_interop

  Disables CUDA/GL interop forcibly. Use on systems that may not behave according to spec (e.g., WSL2 with MESA GL 4.5 software rendering).







## Processing your own Scenes



Our COLMAP loaders expect the following dataset structure in the source path location:



```



|---images

|   |---

|   |---

|   |---...

|---sparse

    |---0

        |---cameras.bin

        |---images.bin

        |---points3D.bin

```



For rasterization, the camera models must be either a SIMPLE_PINHOLE or PINHOLE camera. We provide a converter script ```convert.py```, to extract undistorted images and SfM information from input images. Optionally, you can use ImageMagick to resize the undistorted images. This rescaling is similar to MipNeRF360, i.e., it creates images with 1/2, 1/4 and 1/8 the original resolution in corresponding folders. To use them, please first install a recent version of COLMAP (ideally CUDA-powered) and ImageMagick. Put the images you want to use in a directory ```/input```.

```



|---input

    |---

    |---

    |---...

```

 If you have COLMAP and ImageMagick on your system path, you can simply run 

```shell

python convert.py -s  [--resize] #If not resizing, ImageMagick is not needed

```

Alternatively, you can use the optional parameters ```--colmap_executable``` and ```--magick_executable``` to point to the respective paths. Please note that on Windows, the executable should point to the COLMAP ```.bat``` file that takes care of setting the execution environment. Once done, `````` will contain the expected COLMAP data set structure with undistorted, resized input images, in addition to your original images and some temporary (distorted) data in the directory ```distorted```.



If you have your own COLMAP dataset without undistortion (e.g., using ```OPENCV``` camera), you can try to just run the last part of the script: Put the images in ```input``` and the COLMAP info in a subdirectory ```distorted```:

```



|---input

|   |---

|   |---

|   |---...

|---distorted

    |---database.db

    |---sparse

        |---0

            |---...

```

Then run 

```shell

python convert.py -s  --skip_matching [--resize] #If not resizing, ImageMagick is not needed

```



Command Line Arguments for convert.py



  #### --no_gpu

  Flag to avoid using GPU in COLMAP.

  #### --skip_matching

  Flag to indicate that COLMAP info is available for images.

  #### --source_path / -s

  Location of the inputs.

  #### --camera 

  Which camera model to use for the early matching steps, ```OPENCV``` by default.

  #### --resize

  Flag for creating resized versions of input images.

  #### --colmap_executable

  Path to the COLMAP executable (```.bat``` on Windows).

  #### --magick_executable

  Path to the ImageMagick executable.







## FAQ

- *Where do I get data sets, e.g., those referenced in ```full_eval.py```?* The MipNeRF360 data set is provided by the authors of the original paper on the project site. Note that two of the data sets cannot be openly shared and require you to consult the authors directly. For Tanks&Temples and Deep Blending, please use the download links provided at the top of the page. Alternatively, you may access the cloned data (status: August 2023!) from [HuggingFace](https://huggingface.co/camenduru/gaussian-splatting)



- *How can I use this for a much larger dataset, like a city district?* The current method was not designed for these, but given enough memory, it should work out. However, the approach can struggle in multi-scale detail scenes (extreme close-ups, mixed with far-away shots). This is usually the case in, e.g., driving data sets (cars close up, buildings far away). For such scenes, you can lower the ```--position_lr_init```, ```--position_lr_final``` and ```--scaling_lr``` (x0.3, x0.1, ...). The more extensive the scene, the lower these values should be. Below, we use default learning rates (left) and ```--position_lr_init 0.000016 --scaling_lr 0.001"``` (right).



| ![Default learning rate result](assets/worse.png "title-1")  |  ![Reduced learning rate result](assets/better.png "title-2") |

| --- | --- |



- *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```. What do I do?* 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 /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.



- *24 GB of VRAM for reference quality training is still a lot! Can't we do it with less?* Yes, most likely. By our calculations it should be possible with **way** less memory (~8GB). If we can find the time we will try to achieve this. If some PyTorch veteran out there wants to tackle this, we look forward to your pull request!



- *How can I use the differentiable Gaussian rasterizer for my own project?* Easy, it is included in this repo as a submodule ```diff-gaussian-rasterization```. Feel free to check out and install the package. It's not really documented, but using it from the Python side is very straightforward (cf. ```gaussian_renderer/__init__.py```).



- *Wait, but `````` isn't optimized and could be much better?* There are several parts we didn't even have time to think about improving (yet). The performance you get with this prototype is probably a rather slow baseline for what is physically possible.



- *Something is broken, how did this happen?* We tried hard to provide a solid and comprehensible basis to make use of the paper's method. We have refactored the code quite a bit, but we have limited capacity to test all possible usage scenarios. Thus, if part of the website, the code or the performance is lacking, please create an issue. If we find the time, we will do our best to address it.