+
RoMa ποΈ:
Robust Dense Feature Matching
βCVPR 2024β
+ + Johan Edstedt + Β· + Qiyu Sun + Β· + Georg BΓΆkman + Β· + MΓ₯rten WadenbΓ€ck + Β· + Michael Felsberg +
++ Paper | + Project Page +
+ + ++
+
+
+ RoMa is the robust dense feature matcher capable of estimating pixel-dense warps and reliable certainties for almost any image pair.
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+| [Webpage](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/) | [Full Paper](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/3d_gaussian_splatting_high.pdf) | [Video](https://youtu.be/T_kXY43VZnk) | [Other GRAPHDECO Publications](http://www-sop.inria.fr/reves/publis/gdindex.php) | [FUNGRAPH project page](https://fungraph.inria.fr) |
+| [T&T+DB COLMAP (650MB)](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/input/tandt_db.zip) | [Pre-trained Models (14 GB)](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/pretrained/models.zip) | [Viewers for Windows (60MB)](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/binaries/viewers.zip) | [Evaluation Images (7 GB)](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/evaluation/images.zip) |
+ + +This repository contains the official authors implementation associated with the paper "3D Gaussian Splatting for Real-Time Radiance Field Rendering", which can be found [here](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/). We further provide the reference images used to create the error metrics reported in the paper, as well as recently created, pre-trained models. + +
+
+
+ 
+
+Abstract: *Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (β₯ 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.*
+
+
+
+BibTeX
+@Article{kerbl3Dgaussians,
+ author = {Kerbl, Bernhard and Kopanas, Georgios and Leimk{\"u}hler, Thomas and Drettakis, George},
+ title = {3D Gaussian Splatting for Real-Time Radiance Field Rendering},
+ journal = {ACM Transactions on Graphics},
+ number = {4},
+ volume = {42},
+ month = {July},
+ year = {2023},
+ url = {https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/}
+}
+``` 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.
+
+
+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/+ +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). + +### 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
+
+
+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
+
+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 ```
+
+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 ```
+
+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: + +``` +
+
+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. ++ +### Training speed acceleration + +We integrated the drop-in replacements from [Taming-3dgs](https://humansensinglab.github.io/taming-3dgs/)1 with [fused ssim](https://github.com/rahul-goel/fused-ssim/tree/main) into the original codebase to speed up training times. Once installed, the accelerated rasterizer delivers a **$\times$ 1.6 training time speedup** using `--optimizer_type default` and a **$\times$ 2.7 training time speedup** using `--optimizer_type sparse_adam`. + +To get faster training times you must first install the accelerated rasterizer to your environment: + +```bash +pip uninstall diff-gaussian-rasterization -y +cd submodules/diff-gaussian-rasterization +rm -r build +git checkout 3dgs_accel +pip install . +``` + +Then you can add the following parameter to use the sparse adam optimizer when running `train.py`: + +```bash +--optimizer_type sparse_adam +``` + +*Note that this custom rasterizer has a different behaviour than the original version, for more details on training times please see [stats for training times](results.md/#training-times-comparisons)*. + +*1. Mallick and Goel, et al. βTaming 3DGS: High-Quality Radiance Fields with Limited Resourcesβ. SIGGRAPH Asia 2024 Conference Papers, 2024, https://doi.org/10.1145/3680528.3687694, [github](https://github.com/humansensinglab/taming-3dgs)* + + +### Depth regularization + +To have better reconstructed scenes we use depth maps as priors during optimization with each input images. It works best on untextured parts ex: roads and can remove floaters. Several papers have used similar ideas to improve various aspects of 3DGS; (e.g. [DepthRegularizedGS](https://robot0321.github.io/DepthRegGS/index.html), [SparseGS](https://formycat.github.io/SparseGS-Real-Time-360-Sparse-View-Synthesis-using-Gaussian-Splatting/), [DNGaussian](https://fictionarry.github.io/DNGaussian/)). The depth regularization we integrated is that used in our [Hierarchical 3DGS](https://repo-sam.inria.fr/fungraph/hierarchical-3d-gaussians/) paper, but applied to the original 3DGS; for some scenes (e.g., the DeepBlending scenes) it improves quality significantly; for others it either makes a small difference or can even be worse. For example results showing the potential benefit and statistics on quality please see here: [Stats for depth regularization](results.md). + +When training on a synthetic dataset, depth maps can be produced and they do not require further processing to be used in our method. + +For real world datasets depth maps should be generated for each input images, to generate them please do the following: +1. Clone [Depth Anything v2](https://github.com/DepthAnything/Depth-Anything-V2?tab=readme-ov-file#usage): + ``` + git clone https://github.com/DepthAnything/Depth-Anything-V2.git + ``` +2. Download weights from [Depth-Anything-V2-Large](https://huggingface.co/depth-anything/Depth-Anything-V2-Large/resolve/main/depth_anything_v2_vitl.pth?download=true) and place it under `Depth-Anything-V2/checkpoints/` +3. Generate depth maps: + ``` + python Depth-Anything-V2/run.py --encoder vitl --pred-only --grayscale --img-path