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A 3D Gaussian Splatting framework with various derived algorithms and an interactive web viewer

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Gaussian Splatting PyTorch Lightning Implementation

Known issues

Features

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 torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118

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

    Both the v0 and v1 are supported. The v0 is recommended currently.

    • v0.1.12 (recommended)

      pip install git+https://github.com/yzslab/gsplat.git

      This command will install my modified version, which is required by LightGaussian and Mip-Splatting. If you do not need them, you can also install vanilla gsplat v0.1.12.

    • v1 (beta)

      NOTE: Only my modified v1 is supported

      pip install git+https://github.com/yzslab/gsplat.git@v1-with_v0_interfaces

  • If you need SegAnyGaussian

    • gsplat (see command above)

    • pip install hdbscan scikit-learn==1.3.2 git+https://github.com/facebookresearch/segment-anything.git

    • facebookresearch/pytorch3d

      For torch==2.0.1 and cuda 11.8:

      pip install fvcore iopath
pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py39_cu118_pyt201/download.html

    • 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 \
    ...
# 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 \
  ...

2.3. Use nerfstudio-project/gsplat

Make sure that command which nvcc can produce output, or gsplat will be disabled automatically.

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: image

# 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 git+https://github.com/hbb1/diff-surfel-rasterization.git@3a9357f6a4b80ba319560be7965ed6a88ec951c6

  • Then start training

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

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
image image
image image

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.

(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

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

  • Install submodules/mcmc_relocation first
pip install submodules/mcmc_relocation
  • Then training
... 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
image image image image

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

pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
  • 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]

Metrics of MipNeRF360 dataset One batch per GPU, 30K iterations, no other hyperparameters changed.

  • PSNR image

  • SSIM image

  • LPIPS image

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 diffusers==0.27.2 transformers==4.40.1 scikit-learn

  • 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

    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:

    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 dacite git+https://github.com/yzslab/StopThePop-Rasterization.git

  • 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.

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

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

python viewer.py \
    Deformable-3D-Gaussians/outputs/lego \
    --vanilla_deformable \
    --reorient disable  # change to enable when loading real world scene
python viewer.py \
    4DGaussians/outputs/lego \
    --vanilla_gs4d
# Install `diff-surfel-rasterization` first
pip install git+https://github.com/hbb1/diff-surfel-rasterization.git@28c928a36ea19407cd9754d068bd9a9535216979
# Then start viewer
python viewer.py \
    2d-gaussian-splatting/outputs/Truck \
    --vanilla_gs2d
python viewer.py \
    SegAnyGAussians/outputs/Truck \
    --vanilla_seganygs
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.

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A 3D Gaussian Splatting framework with various derived algorithms and an interactive web viewer

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Sep 25, 2024
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