Gaussian Splatting PyTorch Lightning Implementation

Installation
Training
Web Viewer
Changelog

Known issues

~~Multi-GPU training can only be enabled after densification~~ (Try 2.16. New Multiple GPU training strategy)

Features

Multi-GPU/Node training
Switch between diff-gaussian-rasterization and nerfstudio-project/gsplat
Multiple dataset types support
- Blender (nerf_synthetic)
- Colmap
- PolyCam (See the instruction here)
- Nerfies
- NSVF (Synthetic only)
- MatrixCity (Prepare your dataset)
- PhotoTourism
Interactive web viewer
- Load multiple models
- Model transform
- Scene editor
- Video camera path editor
Video renderer
Load a large number of images without OOM
Dynamic object mask
Derived algorithms
- Deformable Gaussians
  - Deformable 3D Gaussians (2.5.)
  - 4D Gaussian (4.3.) (Viewer Only)
- Mip-Splatting (2.6.)
- LightGaussian (2.7.)
- AbsGS / EfficientGS (2.8.)
- 2D Gaussian Splatting (2.9.)
- Segment Any 3D Gaussians (2.10.)
- Large-scale scene reconstruction with partitioning and LoD (2.11.)
- New Appearance Model (2.12.): improve the quality when images have various appearances
- 3D Gaussian Splatting as Markov Chain Monte Carlo (2.13.)
- Feature distillation (2.14.)
- In the wild (2.15.)
- New Multiple GPU training strategy (2.16.)
- SpotLessSplats (2.17.)
- Depth Regularization with Depth Anything V2 (2.18.)
- StopThePop (2.19.)
- Scale Regularization (2.20.)
- Taming 3DGS (2.21.)

1. Installation

1.1. Clone repository

# clone repository
git clone https://github.com/yzslab/gaussian-splatting-lightning.git
cd gaussian-splatting-lightning

1.2. Create virtual environment

# create virtual environment
conda create -yn gspl python=3.9 pip
conda activate gspl

1.3. Install PyTorch

Tested on PyTorch==2.0.1
You must install the one match to the version of your nvcc (nvcc --version)

For CUDA 11.8

pip install -r requirements/pyt201_cu118.txt

1.4. Install requirements

pip install -r requirements.txt

1.5. Install optional packages

ffmpeg is required if you want to render video: sudo apt install -y ffmpeg
If you want to use nerfstudio-project/gsplat

NOTE: Only my modified v1 is supported
```
pip uninstall -y gsplat
pip install -r requirements/gsplat.txt
```
If you need SegAnyGaussian
- gsplat (see command above)
- SAM
```
pip install -r requirements/sam.txt
```
- facebookresearch/pytorch3d
  
  For torch==2.0.1 and cuda 11.8:
```
pip install -r requirements/pytorch3d-pre.txt
pip install --no-index --no-cache-dir -r requirements/pytorch3d-py39_cu118_pyt201.txt
```
- Download ViT-H SAM model, place it to the root dir of this repo.: wget -O sam_vit_h_4b8939.pth https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

2. Training

2.1. Basic command

python main.py fit \
    --data.path DATASET_PATH \
    -n EXPERIMENT_NAME

It can detect some dataset type automatically. You can also specify type with option --data.parser. Possible values are: Colmap, Blender, NSVF, Nerfies, MatrixCity, PhotoTourism, SegAnyColmap, Feature3DGSColmap.

[NOTE] By default, only checkpoint files will be produced on training end. If you need ply file in vanilla 3DGS's format (can be loaded by SIBR_viewer or some WebGL/GPU based viewer):

[Option 1]: Convert checkpoint file to ply: python utils/ckpt2ply.py TRAINING_OUTPUT_PATH, e.g.:
- python utils/ckpt2ply.py outputs/lego
- python utils/ckpt2ply.py outputs/lego/checkpoints/epoch=300-step=30000.ckpt
[Option 2]: Start training with option: --model.save_ply true

2.2. Some useful options

Run training with web viewer

python main.py fit \
    --viewer \
    ...

It is recommended to use config file configs/blender.yaml when training on blender dataset.

python main.py fit \
    --config configs/blender.yaml \
    ...

With mask (colmap dataset only)
- You may need to undistort mask images too: utils/colmap_undistort_mask.py

# the requirements of mask
#   * must be single channel
#   * zero(black) represent the masked pixel (won't be used to supervise learning)
#   * the filename of the mask file must be image filename + '.png', 
#     e.g.: the mask of '001.jpg' is '001.jpg.png'
... fit \
  --data.parser Colmap \
  --data.parser.mask_dir MASK_DIR_PATH \
  ...

Use downsampled images (colmap dataset only)

You can use utils/image_downsample.py to downsample your images, e.g. 4x downsample: python utils/image_downsample.py PATH_TO_DIRECTORY_THAT_STORE_IMAGES --factor 4

# it will load images from `images_4` directory
... fit \
  --data.parser Colmap \
  --data.parser.down_sample_factor 4 \
  ...

Rounding mode is specified by --data.parser.down_sample_rounding_mode. Available values are floor, round, round_half_up, ceil. Default is round.

Load large dataset without OOM
- [1st option] Cache images in uint8 data type
```
... fit \
    --data.image_uint8 true
```
- [2nd option] Limit the maximum number of the cached images
  - Cache the next batch during training (Recommended)
```
... fit \
  --data.train_max_num_images_to_cache 512 \
  --data.async_caching true \
  ...
```
  - Cache the next batch at the end of the current batch
```
... fit \
  --data.train_max_num_images_to_cache 1024 \
  ...
```
Speedup training
- Store all images in GPU memory
```
... fit \
  --data.image_on_cpu false \
  ...
```
  Combining this with --data.image_uint8 true to reduce GPU memory consumption is also feasible.
- Avoid performing a validation after every training epoch
  
  Simply set check_val_every_n_epoch to a very large value. Please note that the metrics starting with val/, such as val/psnr, on the progress bar will not be updated during the training.
```
... fit \
  --trainer.check_val_every_n_epoch 99999 \
  ...
```
- Take a look at Taming 3DGS (2.21.) for further acceleration

2.3. Use nerfstudio-project/gsplat

python main.py fit \
    --config configs/gsplat.yaml \
    ...

2.4. Multi-GPU training (DDP)

[NOTE] Try New Multiple GPU training strategy, which can be enabled during densification.

[NOTE] Multi-GPU training with DDP strategy can only be enabled after densification. You can start a single GPU training at the beginning, and save a checkpoint after densification finishing. Then resume from this checkpoint and enable multi-GPU training.

You will get improved PSNR and SSIM with more GPUs:

# Single GPU at the beginning
python main.py fit \
    --config ... \
    --data.path DATASET_PATH \
    --model.density.densify_until_iter 15000 \
    --max_steps 15000
# Then resume, and enable multi-GPU
python main.py fit \
    --config ... \
    --trainer configs/ddp.yaml \
    --data.path DATASET_PATH \
    --max_steps 30000 \
    --ckpt_path last  # find latest checkpoint automatically, or provide a path to checkpoint file

2.5. Deformable 3D Gaussians

deform-gs-new.mp4

python main.py fit \
    --config configs/deformable_blender.yaml \
    --data.path ...

2.6. Mip-Splatting

Training:

python main.py fit \
    --config configs/mip_splatting_gsplat_v2.yaml \
    --data.path ...

Fuse the 3D smoothing filter to the Gaussian parameters:

python utils/fuse_mip_filter.py \
    TRAINED_MODEL_DIR

2.7. LightGaussian

Prune & finetune only currently

Train & densify & prune

... fit \
    --config configs/light_gaussian/train_densify_prune-gsplat.yaml \
    --data.path ...

Prune & finetune (make sure to use the same hparams as the input model used)

... fit \
    --config configs/light_gaussian/prune_finetune-gsplat.yaml \
    --data.path ... \
    ... \
    --ckpt_path YOUR_CHECKPOINT_PATH

2.8. AbsGS / EfficientGS

... fit \
    --config configs/gsplat-absgrad.yaml \
    --data.path ...

2.9. 2D Gaussian Splatting

Install diff-surfel-rasterization first
```
pip install -r requirements/2DGS.txt
```

Then start training

... fit \
    --config configs/vanilla_2dgs.yaml \
    --data.path ...

Mesh extraction

Bounded

python utils/gs2d_mesh_extraction.py MODEL_OUTPUT_PATH

Unbounded

python utils/gs2d_mesh_extraction.py MODEL_OUTPUT_PATH --unbounded true

2.10. Segment Any 3D Gaussians

First, train a 3DGS scene using gsplat

python main.py fit \
    --config configs/gsplat.yaml \
    --data.path data/Truck \
    -n Truck -v gsplat  # trained model will save to `outputs/Truck/gsplat`

Then generate SAM masks and their scales

Masks
```
python utils/get_sam_masks.py data/Truck/images
```
You can specify the path to SAM checkpoint via argument -c PATH_TO_SAM_CKPT

Scales

python utils/get_sam_mask_scales.py outputs/Truck/gsplat

Both the masks and scales will be saved in data/Truck/semantics, the structure of data/Truck will like this:

├── images  # The images of your dataset
    ├── 000001.jpg
    ├── 000002.jpg
    ...
├── semantic  # Generated by `get_sam_masks.py` and `get_sam_mask_scales.py`
    ├── masks
        ├── 000001.jpg.pt
        ├── 000002.jpg.pt
        ...
    └── scales
        ├── 000001.jpg.pt
        ├── 000002.jpg.pt
        ...
├── sparse  # colmap sparse database
    ...

Train SegAnyGS

python seganygs.py fit \
    --config configs/segany_splatting.yaml \
    --data.path data/Truck \
    --model.initialize_from outputs/Truck/gsplat \
    -n Truck -v seganygs  # save to `outputs/Truck/seganygs`

The value of --model.initialize_from is the path to the trained 3DGS model

Start the web viewer to perform segmentation or cluster
```
python viewer.py outputs/Truck/seganygs
```
SegAnyGS-WebViewer.mp4

2.11. Large-scale scene reconstruction with partitioning and LoD

Baseline	Partitioning

The implementation here references Block-NeRF, VastGaussian and CityGaussians.

There is no single script to finish the whole pipeline. Please refer to below contents about how to reconstruct a large scale scene.

Partitioning
- MatrixCity: notebooks/matrix_city_split.ipynb (Refer to MatrixCity.md about preparing MatrixCity dataset)
- Colmap: notebooks/colmap_split_v2.ipynb
Training

[NOTE] You must explicitly specify using a gsplat-based renderer (e.g., configs/gsplat.yaml, configs/appearance_embedding_renderer, configs/mip_splatting_gsplat.yaml, etc) because some parts of the pipeline make assumptions about this. If you do not, it will use diff-gaussian-rasterization by default, which will cause a drop in the final quality.
- MatrixCity: utils/train_matrix_city_partitions_v2.py
- Colmap: utils/train_colmap_partitions_v2.py
Optional LightGaussian pruning
- Pruning: utils/prune_partitions_v2.py
- Finetune after pruning: utils/finetune_pruned_partitions_v2.py
Merging: utils/merge_partitions_v2.py
LoD: internal/renderers/partition_lod_renderer.py

(1) An example pipeline for the Rubble dataset from MegaNeRF

First prepare the dataset

# 1. download the dataset
mkdir -p data/MegaNeRF
pushd data/MegaNeRF
wget -O rubble-pixsfm.tgz https://storage.cmusatyalab.org/mega-nerf-data/rubble-pixsfm.tgz
tar -zxf rubble-pixsfm.tgz
popd

# 2. create a colmap sparse model from provided camera poses
wget -O vocab_tree_flickr100K_words256K.bin https://demuc.de/colmap/vocab_tree_flickr100K_words256K.bin
python utils/meganerf2colmap.py data/MegaNeRF/rubble-pixsfm -v vocab_tree_flickr100K_words256K.bin

# 3. down sample images
python utils/image_downsample.py data/MegaNeRF/rubble-pixsfm/colmap/images --factor 3

The intrinsics and extrinsics provided in the Rubble dataset seem not optimal and will produce a slightly blurry result. Run a sparse reconstruction from scratch if you want to avoid it.

Generate appearance groups

The Rubble dataset contains images in various lighting conditions. Enabling the appearance model can improve the quality. In order to do so, appearance groups must be generated first.
```
python utils/generate_image_apperance_groups.py \
    data/MegaNeRF/rubble-pixsfm/colmap \
    --image \
    --name appearance_image_dedicated
```
Partitioning: simply use notebooks/meganerf_rubble_split.ipynb, and remember to change the value of dataset_path in this notebook
Training
```
PARTITION_DATA_PATH="data/MegaNeRF/rubble-pixsfm/colmap/partitions-size_60.0-enlarge_0.1-visibility_0.9_0.25"
PROJECT_NAME="MegaNeRF-rubble"

python utils/train_colmap_partitions_v2.py \
    ${PARTITION_DATA_PATH} \
    -p ${PROJECT_NAME} \
    --scalable-config utils/scalable_param_configs/appearance.yaml \
    --config configs/appearance_embedding_renderer/sh_view_dependent.yaml \
    -- \
    --data.parser.appearance_groups appearance_image_dedicated \
    --model.gaussian.optimization.spatial_lr_scale 15 \
    --data.parser.down_sample_factor 3
```
Parameter Explanations
- utils/train_colmap_partitions_v2.py
  - The first is the path of the directory containing the partition data, it is the value of output_path in the partitioning notebook
  - -p: the project name, all the trained partition models will be stored in outputs/PROJECT_NAME
  - --scalable-config: the path of the yaml file specifying the hyperparameters that will be adjusted according to the image number
  - --config: the config file used for training
  - --: all the parameters after this will be passed to main.py as-is
- main.py
  - --data.parser.appearance_groups: the name of the generated appearance groups
  - --model.gaussian.optimization.spatial_lr_scale: the LR of the 3D means of Gaussians will be multiplied by this value, which determines how finely the structure can be modeled
    
    By default, this value will be calculated automatically according to the camera poses if it is omitted, but this will lead to suboptimal results for large-scale scenes. Therefore you had better provide it manually.
    
    Please note that this is a scene-specific hyperparameter. You should not use the same value for another scene, or even when you run a colmap sparse reconstruction a second time. Additionally, the value 15 is not the optimal one for the dataset Rubble.
  - --data.parser.down_sample_factor: down sample factor of the images

Merging

python utils/merge_partitions_v2.py \
    ${PARTITION_DATA_PATH} \
    -p ${PROJECT_NAME}

Then you can start the web viewer with the merged checkpoint file.

Optional prune and finetune

Prune

python utils/prune_partitions_v2.py \
    ${PARTITION_DATA_PATH} \
    -p ${PROJECT_NAME}

Finetune

PRUNED_PROJECT_NAME="${PROJECT_NAME}-pruned"
python utils/finetune_pruned_partitions_v2.py \
    ${PARTITION_DATA_PATH} \
    -p ${PRUNED_PROJECT_NAME} \
    -t ${PROJECT_NAME} \
    --scalable-config utils/scalable_param_configs/appearance.yaml

It will load trained model from outputs/${PROJECT_NAME}, and the finetuned outputs will be saved to outputs/${PRUNED_PROJECT_NAME}.

Merge finetuned outputs

python utils/merge_partitions_v2.py \
    ${PARTITION_DATA_PATH} \
    -p ${PRUNED_PROJECT_NAME}

LoD Rendering

With LoD, the renderer will select the finer models for partitions close to the camera, and coarser models for those far away.

(a) Preprocess partitions' checkpoints

This is done by simply add an option --preprocess when running utils/merge_partitions_v2.py. You need to run it for every level.

# First for the original models
python utils/merge_partitions_v2.py \
    --preprocess \
    ${PARTITION_DATA_PATH} \
    -p ${PROJECT_NAME}

# Then for the pruned models
python utils/merge_partitions_v2.py \
    --preprocess \
    ${PARTITION_DATA_PATH} \
    -p ${PRUNED_PROJECT_NAME}

(b) Create a LoD config file in YAML

# `data` is the path to your partition data
data: data/MegaNeRF/rubble-pixsfm/colmap/partitions-size_60.0-enlarge_0.1-visibility_0.9_0.25
# `names` is a list of project names, where order represents their detail levels, ranging from fine to coarse
names:
  - MegaNeRF-rubble  # without pruning
  - MegaNeRF-rubble-pruned  # pruned
  # - MegaNeRF-rubble-pruned_again  # add more lines if you have

(c) Start the viewer

python viewer YOU_LOD_CONFIG_FILE_PATH.yaml

(2) Utilize multiple GPUs

Train/prune/finetune multiple partitions in parallel

The options --n-processes and --process-id are designed for the parallel purpose. --n-processes is the total number of GPUs you want to use, which has the same meaning as world size. --process-id is the unique process id, just like the global rank, but ranging from 1 to the value of --n-processes here. Please note that both of the options should be placed before --.

Assuming you have 2 machines, and both of them are equipped with 2 GPUs:
- On the first machine
  
  First run:
```
CUDA_VISIBLE_DEVICES=0 python utils/train_colmap_partitions_v2.py \
    ... \
    --n-processes 4 \
    --process-id 1 \
    -- \
    ...
```
  Then run this in another terminal: CUDA_VISIBLE_DEVICES=1 python ... --n-processes 4 --process-id 2
- On the second machine
  
  First: CUDA_VISIBLE_DEVICES=0 python ... --n-processes 4 --process-id 3
  
  Then another terminal:CUDA_VISIBLE_DEVICES=1 python ... --n-processes 4 --process-id 4
Or submit partition training jobs to Slurm:
```
# Adding params after the 2nd `--` means submitting jobs to Slurm,
#   and those params belong to `srun`.
# Do not add `--n-processes` and `--process-id` here.
python utils/train_colmap_partitions_v2.py \
   ... \
   -- \
   ... \
   -- \
   --gres=gpu:1  # one gpu per-partition training job
```
Train/finetune a partition with multiple GPUs
- Training only works with the strategy introduced in 2.16.
- Finetune only works with DDP

2.12. Appearance Model

With appearance model, the reconstruction quality can be improved when your images have various appearance, such as different exposure, white balance, contrast and even day and night.

This model assign an extra feature vector $\boldsymbol{\ell}^{(g)}$ to each 3D Gaussian and an appearance embedding vector $\boldsymbol{\ell}^{(a)}$ to each appearance group. Both of them will be used as the input of a lightweight MLP to calculate the color.

$$ \mathbf{C} = f \left ( \boldsymbol{\ell}^{(g)}, \boldsymbol{\ell}^{(a)} \right ) $$

Please refer to internal/renderers/gsplat_appearance_embedding_renderer.py for more details.

Baseline	New Model
Train-head-baseline.mp4	Train-head.mp4
	Day-and-Night.mp4

First generate appearance groups (Colmap or PhotoTourism dataset only)
```
python utils/generate_image_apperance_groups.py PATH_TO_DATASET_DIR \
    --image \
    --name appearance_image_dedicated  # the name will be used later
```
The images in a group will share a common appearance embedding. The command above will assign each image a group, which means that will not share any appearance embedding between images.

Then start training

python main.py fit \
    --config configs/appearance_embedding_renderer/view_dependent.yaml \
    --data.path PATH_TO_DATASET_DIR \
    --data.parser Colmap \
    --data.parser.appearance_groups appearance_image_dedicated  # value here should be the same as the one provided to `--name` above

If you are using PhotoTourism dataset, please replace --data.parser Colmap with --data.parser PhotoTourism.

Other available configs
- view_independent.yaml: turn off view dependent effects
- sh_view_dependent.yaml: represent view dependent effects using spherical harmonics
- *-distributed.yaml: multiple GPUs
- *-estimated_depth_reg.yaml / *-estimated_depth_reg-hard_depth.yaml: with depth regularization
Remove the dependence on MLP when rendering

It is recommended to use view_independent-* or sh_view_dependent-* configs if you want to do so.

By running python utils/fuse_appearance_embeddings_into_shs_dc.py TRAINED_MODEL_DIR, you can get a fixed appearance checkpoint without requiring a MLP.

2.13. 3DGS-MCMC

... fit \
    --config configs/gsplat-mcmc.yaml \
    --model.density.cap_max MAX_NUM_GAUSSIANS \
    ...

MAX_NUM_GAUSSIANS is the maximum number of Gaussians that will be used.

Refer to ubc-vision/3dgs-mcmc, internal/density_controllers/mcmc_density_controller.py and internal/metrics/mcmc_metrics.py for more details.

2.14. Feature distillation

Click me

This comes from Feature 3DGS. But two stage optimization is adapted here, rather than jointly.

First, train a model using gsplat (see command above)
Then extract feature map from your dataset

Theoretically, any feature is distillable. You need to implement your own feature map extractor. Here are instructions about extracting SAM and LSeg features.
- SAM
```
python utils/get_sam_embeddings.py data/Truck/images
```
  With this command, feature maps will be saved to data/Truck/semantic/sam_features, and preview to data/Truck/semantic/sam_feature_preview, respectively.
- LSeg: please use ShijieZhou-UCLA/feature-3dgs and follow its instruction to extra LSeg features (do not use this repo's virtual environment for it).
Then start distillation
- SAM
```
python main.py fit \
    --config configs/feature_3dgs/sam-speedup.yaml \
    --data.path data/Truck \
    --data.parser.down_sample_factor 2 \
    --model.initialize_from outputs/Truck/gsplat \
    -n Truck -v feature_3dgs-sam
```
- LSeg
  
  [NOTE] In order to distill LSeg's high-dimensional features, you may need a GPU equipped with a large memory capacity
```
python main.py fit \
    --config configs/feature_3dgs/lseg-speedup.yaml \
    ...
```
--model.initialize_from is the path to your trained model.

Since rasterizing high dimension features is slow, --data.parser.down_sample_factor is used here to smaller the rendered feature map to speedup distillation.
After distillation finishing, you can use viewer to visualize the feature map rendered from 3D Gaussians
```
python viewer.py outputs/Truck/feature_3dgs
```
CLIP is required if you are using LSeg feature: pip install git+https://github.com/openai/CLIP.git

object-recognition.mp4

LSeg feature is used in this video.

2.15. In the wild

Introduction

Based on the Appearance Model (2.12.) above, this model can produce a visibility map for every training view indicating whether a pixel belongs to transient objects or not.

The idea of the visibility map is a bit like Ha-NeRF, but rather than uses positional encoding for pixel coordinates, 2D dense grid encoding is used here in order to accelerate training.

Please refer to Ha-NeRF, internal/renderers/gsplat_appearance_embedding_visibility_map_renderer.py and internal/metrics/visibility_map_metrics.py for more details.

[NOTE] Though it shows the capability to distinguish the pixels of transient objects, may not be able to remove some artifats/floaters belong to transients. And may also treat under-reconstructed regions as transients.

Usage

tiny-cuda-nn is required

pip install -r requirements/tcnn.txt

Preparing dataset

Download PhotoTourism dataset from here and split file from the "Additional links" here. The split file should be placed at the same path as the dense directory of the PhotoTourism dataset, e.g.:

├──brandenburg_gate
  ├── dense  # colmap database
      ├── images
          ├── ...
      ├── sparse
      ...
  ├── brandenburg.tsv  # split file

[Optional] 2x downsize the images: python utils/image_downsample.py data/brandenburg_gate/dense/images --factor 2

Start training

python main.py fit \
    --config configs/appearance_embedding_visibility_map_renderer/view_independent-2x_ds.yaml \
    --data.path data/brandenburg_gate \
    -n brandenburg_gate

If you have not downsized images, remember to add a --data.parser.down_sample_factor 1 to the command above.

Validation on training set

python main.py validate \
   --save_val \
   --val_train \
   --config outputs/brandenburg_gate/lightning_logs/version_0/config.yaml  # you may need to change this path

Then you can find the rendered masks and images in outputs/brandenburg_gate/val.

2.16. New Multiple GPU training strategy

Introduction

This is a bit like a simplified version of Scaling Up 3DGS.

In the implementation here, Gaussians are stored, projected and their colors are calculated in a distributed manner, and each GPU rasterizes a whole image for a different camera. No Pixel-wise Distribution currently.

This strategy works with densification enabled.

[NOTE]

Not well validated yet, still under development
Multiple GPUs training only currently
In order to combine with derived algorithms containing neural networks, you need to manually wrap your networks with DDP, e.g.: internal/renderers/gsplat_distributed_appearance_embedding_renderer.py

Metrics of MipNeRF360 dataset

One batch per GPU, 30K iterations, no other hyperparameters changed.

PSNR
SSIM
LPIPS

Usage

Training

python main.py fit \
    --config configs/distributed.yaml \
    ...

By default, all processes will hold a (redundant) replica of the dataset in memory, which may cause CPU OOM. You can avoid this by adding the option --data.distributed true, so that each process loads a different subset of the dataset.

Merge checkpoints

python utils/merge_distributed_ckpts.py outputs/TRAINED_MODEL_DIR

Start viewer

python viewer.py outputs/TRAINED_MODEL_DIR/checkpoints/MERGED_CHECKPOINT_FILE

2.17. SpotLessSplats

[NOTE] No utilization-based pruning (4.2.3 of the paper) and appearance modeling (4.2.4 of the paper)

Install requirements

pip install -r requirements/SpotLessSplats.txt

Extract Stable Diffusion features

python utils/sd_feature_extraction.py YOUR_IMAGE_DIR

Training
- Spatial clustering (SLS-agg, 4.1.1)
```
python main.py fit \
    --config configs/spot_less_splats/gsplat-cluster.yaml \
    --data.parser.split_mode "reconstruction" \
    --data.path YOUR_DATASET_PATH \
    -n EXPERIMENT_NAME
```
- Spatio-temporal clustering (SLS-mlp, 4.1.2)
```
python main.py fit \
    --config configs/spot_less_splats/gsplat-mlp.yaml \
    --data.parser.split_mode "reconstruction" \
    --data.path YOUR_DATASET_PATH \
    -n EXPERIMENT_NAME
```
- Other available configs
  - gsplat-mlp-with_ssim.yaml: with SSIM metric
  - gsplat-mlp-opacity_reg_0.01.yaml: with opacity regularization, aiming to reduce floaters/artifacts
  - view_independent-phototourism-sls-opacity_reg_0.01.yaml: with new appearance model (2.12.) (not the one mentioned in the SLS paper)
Change the value of --data.parser.split_mode to keyword if you are using the RobustNeRF dataset.

Render SLS predicted masks

python utils/render_sls_masks.py outputs/EXPERIMENT_NAME

2.18. Depth Regularization with Depth Anything V2

This is implemented with reference to Hierarchical 3DGS.

Baseline	DepthReg	DepthReg + AppearanceModel
baseline.mp4	depth_reg.mp4	appearance-depth_reg.mp4

Setup Depth Anything V2

# clone the repo.
git clone https://github.com/DepthAnything/Depth-Anything-V2 utils/Depth-Anything-V2

# NOTE: do not run `pip install -r utils/Depth-Anything-V2/requirements.txt`

# download the pretrained model `Depth-Anything-V2-Large`
mkdir utils/Depth-Anything-V2/checkpoints
wget -O utils/Depth-Anything-V2/checkpoints/depth_anything_v2_vitl.pth "https://huggingface.co/depth-anything/Depth-Anything-V2-Large/resolve/main/depth_anything_v2_vitl.pth?download=true"

Dataset pre-processing

python utils/estimate_dataset_depths.py data/Family

Make sure that both the sparse and images folders exist in data/Family.

With the operation above, the structure of data/Family should be like this:

├── data/Family
    ├── estimated_depths  # generated by `utils/run_depth_anything_v2.py`
        ├── 00001.jpg.npy
        ├── ...
    ├── images
        ├── 00001.jpg
        ├── ...
    ├── sparse  # colmap sparse model
        ├── ...
    ├── estimated_depth_scales.json  # generated by `utils/get_depth_scales.py`
    ...

Training
```
python main.py fit \
    --config configs/depth_regularization/estimated_inverse_depth-l1.yaml \
    --data.path data/Family \
    -n EXPERIMENT_NAME
```
Other available configs:
- estimated_inverse_depth-l1_ssim.yaml: with SSIM as an extra depth metric
- estimated_inverse_depth-l2.yaml: L2 depth loss
- estimated_inverse_depth-hard_depth-l1.yaml: better at removing floaters/artifacts
- estimated_inverse_depth-hard_depth-l1_ssim.yaml
- with new appearance model (2.12.)
  - view_dependent-estimated_depth_reg.yaml
  - view_dependent-estimated_depth_reg-hard_depth.yaml
In my experiments, simply L1 is slightly better than L2 or the one with SSIM.

2.19. StopThePop

Install the StopThePop-Rasterization first:
```
pip install -r requirements/StopThePop.txt
```

Training:

python main.py fit \
    --config configs/stp/baseline.yaml \
    --data.path ... \
    ...

2.20. Scale Regularization

The scales of Gaussians will grow to some unreasonable values after densification. For example, some linear shape Gaussians are almost longer than your scene, and appear as artifacts at many viewpoints. This regularization, containing max scale and scale ratio losses, can avoid it. Take a look internal/metrics/scale_regularization_metrics.py for more details.

Usage:

python main.py fit \
    --config configs/scale_reg.yaml \
    --model.metric.max_scale 1. \
    ...

The --model.metric.max_scale is a scene-specific hyperparameter. The regularization will be applied to the Gaussians with scales exceeding it. It should be greater than percent_dense * camera_extent. The percent_dense is 0.01 by default. The camera_extent will be printed as spatial_lr_scale=... at the beginning of the training. Set it to a very large value, e.g. 2048, to disable the max scale loss if you are not sure what value should be used.

2.21. Taming 3DGS

There are two implementations: one is the gsplat v1 based, and the other is the vanilla one. The gsplat v1 based implementation currently does not have "Backpropagation with Per-Splat Parallelization."

(a) Install libraries first
- fused-ssim
```
pip install -r requirements/fused-ssim.txt
```
- my modified gsplat v1 if you want the gsplat v1 based one (refer to 1.5. for the setup guide)
- another rasterizer if you want the vanilla one
```
pip install -r requirements/diff-accel-rasterization.txt
```

(b) Available config files

Type	gsplat v1	vanilla
Competitive quality	`configs/gsplat_v1-accel.yaml`	`configs/taming_3dgs/rasterizer-fused_ssim-aa.yaml`
More acceleration, slightly lower quality (SparseAdam)	`configs/gsplat_v1-accel_more.yaml`	`configs/taming_3dgs/rasterizer-fused_ssim-sparse_adam-aa.yaml`
Steerable Densification	`configs/gsplat_v1-accel-steerable.yaml`	-

You need to adjust the --model.density.budget if you want to use the steerable one.

3. Evaluation

Per-image metrics will be saved to TRAINING_OUTPUT/metrics as a csv file.

Evaluate on validation set

python main.py validate \
    --config outputs/lego/config.yaml

On test set

python main.py test \
    --config outputs/lego/config.yaml

On train set

python main.py validate \
    --config outputs/lego/config.yaml \
    --val_train

Save images that rendered during evaluation/test

python main.py <validate or test> \
    --config outputs/lego/config.yaml \
    --save_val

Then you can find the images in outputs/lego/<val or test>.

4. Web Viewer

Transform	Camera Path	Edit
transform.mp4	animation.mp4	edit.mp4

4.1 Basic usage

Also works for graphdeco-inria/gaussian-splatting's ply output

python viewer.py TRAINING_OUTPUT_PATH
# e.g.: 
#   python viewer.py outputs/lego/
#   python viewer.py outputs/lego/checkpoints/epoch=300-step=30000.ckpt
#   python viewer.py outputs/lego/baseline/point_cloud/iteration_30000/point_cloud.ply  # only works with VanillaRenderer

4.2 Load multiple models and enable transform options

python viewer.py \
    outputs/garden \
    outputs/lego \
    outputs/Synthetic_NSVF/Palace/point_cloud/iteration_30000/point_cloud.ply \
    --enable_transform

4.3 Load model trained by other implementations

[NOTE] The commands in this section only design for third-party outputs

ingra14m/Deformable-3D-Gaussians

python viewer.py \
    Deformable-3D-Gaussians/outputs/lego \
    --vanilla_deformable \
    --reorient disable  # change to enable when loading real world scene

hustvl/4DGaussians

python viewer.py \
    4DGaussians/outputs/lego \
    --vanilla_gs4d

hbb1/2d-gaussian-splatting

# Install `diff-surfel-rasterization` first
pip install -r requirements/diff-surfel-rasterization.txt
# Then start viewer
python viewer.py \
    2d-gaussian-splatting/outputs/Truck \
    --vanilla_gs2d

Jumpat/SegAnyGAussians

python viewer.py \
    SegAnyGAussians/outputs/Truck \
    --vanilla_seganygs

autonomousvision/mip-splatting

python viewer.py \
    mip-splatting/outputs/bicycle \
    --vanilla_mip

5. F.A.Q.

Q: The viewer shows my scene in unexpected orientation, how to rotate the camera, like the U and O key in the SIBR_viewer?

A: Check the Orientation Control on the right panel, rotate the camera frustum in the scene to the orientation you want, then click Apply Up Direction.

reorient-camera-up.mp4

Besides: You can also click the 'Reset up direction' button. Then the viewer will use your current orientation as the reference.

First use mouse to rotate your camera to the orientation you want
Then click the 'Reset up direction' button

Q: The web viewer is slow (or low fps, far from real-time).

A: This is expected because of the overhead of the image transfer over network. You can get around 10fps in 1080P resolution, which is enough for you to view the reconstruction quality.

Files

README.md

Latest commit

History

README.md

File metadata and controls

Gaussian Splatting PyTorch Lightning Implementation

Known issues

Features

1. Installation

1.1. Clone repository

1.2. Create virtual environment

1.3. Install PyTorch

1.4. Install requirements

1.5. Install optional packages

2. Training

2.1. Basic command

2.2. Some useful options

2.3. Use nerfstudio-project/gsplat

2.4. Multi-GPU training (DDP)

2.5. Deformable 3D Gaussians

2.6. Mip-Splatting

2.7. LightGaussian

2.8. AbsGS / EfficientGS

2.9. 2D Gaussian Splatting

2.10. Segment Any 3D Gaussians

2.11. Large-scale scene reconstruction with partitioning and LoD

(1) An example pipeline for the Rubble dataset from MegaNeRF

(2) Utilize multiple GPUs

2.12. Appearance Model

2.13. 3DGS-MCMC

2.14. Feature distillation

2.15. In the wild

Introduction

Usage

2.16. New Multiple GPU training strategy

Introduction

Usage

2.17. SpotLessSplats

2.18. Depth Regularization with Depth Anything V2

2.19. StopThePop

2.20. Scale Regularization

2.21. Taming 3DGS

3. Evaluation

Evaluate on validation set

On test set

On train set

Save images that rendered during evaluation/test

4. Web Viewer

4.1 Basic usage

4.2 Load multiple models and enable transform options

4.3 Load model trained by other implementations

5. F.A.Q.