Feature 3DGS 🪄: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields

Shijie Zhou, Haoran Chang*, Sicheng Jiang*, Zhiwen Fan, Zehao Zhu, Dejia Xu, Pradyumna Chari, Suya You, Zhangyang Wang, Achuta Kadambi (* indicates equal contribution)
| Webpage | Full Paper | Video |

Abstract: 3D scene representations have gained immense popularity in recent years. Methods that use Neural Radiance fields are versatile for traditional tasks such as novel view synthesis. In recent times, some work has emerged that aims to extend the functionality of NeRF beyond view synthesis, for semantically aware tasks such as editing and segmentation using 3D feature field distillation from 2D foundation models. However, these methods have two major limitations: (a) they are limited by the rendering speed of NeRF pipelines, and (b) implicitly represented feature fields suffer from continuity artifacts reducing feature quality. Recently, 3D Gaussian Splatting has shown state-of-the-art performance on real-time radiance field rendering. In this work, we go one step further: in addition to radiance field rendering, we enable 3D Gaussian splatting on arbitrary-dimension semantic features via 2D foundation model distillation. This translation is not straightforward: naively incorporating feature fields in the 3DGS framework leads to warp-level divergence. We propose architectural and training changes to efficiently avert this problem. Our proposed method is general, and our experiments showcase novel view semantic segmentation, language-guided editing and segment anything through learning feature fields from state-of-the-art 2D foundation models such as SAM and CLIP-LSeg. Across experiments, our distillation method is able to provide comparable or better results, while being significantly faster to both train and render. Additionally, to the best of our knowledge, we are the first method to enable point and bounding-box prompting for radiance field manipulation, by leveraging the SAM model.

BibTeX

@article{zhou2023feature,
  author    = {Zhou, Shijie and Chang, Haoran and Jiang, Sicheng and Fan, Zhiwen and Zhu, Zehao and Xu, Dejia and Chari, Pradyumna and You, Suya and Wang, Zhangyang and Kadambi, Achuta},
  title     = {Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields},
  journal   = {arXiv preprint arXiv:2312.03203},
  year      = {2023},
}

Processing your own Scenes

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

<location>
|---images
|   |---<image 0>
|   |---<image 1>
|   |---...
|---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 <location>/input.

<location>
|---input
    |---<image 0>
    |---<image 1>
    |---...

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

python convert.py -s <location> [--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, <location> 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:

<location>
|---input
|   |---<image 0>
|   |---<image 1>
|   |---...
|---distorted
    |---database.db
    |---sparse
        |---0
            |---...

Then run

python convert.py -s <location> --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.

Feature Encoding from Teacher Network

LSeg encoder

Installation

Setup LSeg

cd encoders/lseg_encoder
pip install -r requirements.txt
pip install git+https://github.com/zhanghang1989/PyTorch-Encoding/

Download the LSeg model file demo_e200.ckpt from the Google drive.

Feature embedding

python -u encode_images.py --backbone clip_vitl16_384 --weights demo_e200.ckpt --widehead --no-scaleinv --outdir ../../data/DATASET_NAME/rgb_feature_langseg --test-rgb-dir ../../data/DATASET_NAME/images --workers 0

This may produces large feature map files in --outdir (100-200MB per file).

Run train.py. If reconstruction fails, change --scale 4.0 to smaller or larger values, e.g., --scale 1.0 or --scale 16.0.

SAM encoder

Installation

The code requires python>=3.8, as well as pytorch>=1.7 and torchvision>=0.8. Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.

SAM setup:

cd encoders/sam_encoder
pip install -e .

Pretrain model download:

Click the links below to download the checkpoint for the corresponding model type.

• default or vit_h: ViT-H SAM model.
• vit_l: ViT-L SAM model.
• vit_b: ViT-B SAM model.

And place it at the folder: encoders/sam_encoder/checkpoints

The following optional dependencies are necessary for mask post-processing, saving masks in COCO format.

pip install opencv-python pycocotools matplotlib onnxruntime onnx

Feature embedding

Run the following to export the image embeddings of an input image or directory of images.

python export_image_embeddings.py --checkpoint checkpoints/sam_vit_h_4b8939.pth --model-type vit_h --input ../../data/DATASET_NAME/images  --output ../../data/OUTPUT_NAME/sam_embeddings

Training, Rendering, and Inference:

Train

python train.py -s data/DATASET_NAME -m output/OUTPUT_NAME -f lseg -r 0 --speedup

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/<random> by default).

--foundation_model / -f

Switch different foundation model encoders, lseg for LSeg and sam for SAM

--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. If proveided 0, use GT feature map's resolution. For all other values, rescales the width to the given number while maintaining image aspect. If proveided -2, use the customized resolution. If not set and input image width exceeds 1.6K pixels, inputs are automatically rescaled to this target.

--speedup

Optional speed-up module for reduced feature dimention initialization.

--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.

--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 <iterations> 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.

Gaussian Rasterization with High-dimensional Features

You can customize NUM_SEMANTIC_CHANNELS in submodules/diff-gaussian-rasterization/cuda_rasterizer/config.h for any number of feature dimension that you want:

• Customize NUM_SEMANTIC_CHANNELS in config.h.

If you would like to use the optional CNN speed-up module, do the following accordingly:

• Customize NUMBER in semantic_feature_size/NUMBER in scene/gaussian_model.py in line 142.
• Customize NUMBER in feature_out_dim/NUMBER in train.py in line 51.
• Customize NUMBER in feature_out_dim/NUMBER in render.py in line 116 and 246.

where feature_out_dim / NUMBER = NUM_SEMANTIC_CHANNELS. The feature_out_dim matches the ground truth foundation model dimensions, 512 for LSeg and 256 for SAM. The default NUMBER = 2. For your reference, here are 4 configurations of runing train.py:

For langage-guided editing:

-f lseg with NUM_SEMANTIC_CHANNELS 512*.

For segmentation tasks:

-f lseg --speedup with NUM_SEMANTIC_CHANNELS 256, NUMBER = 2*.

-f sam with NUM_SEMANTIC_CHANNELS 256.

-f sam --speedup with NUM_SEMANTIC_CHANNELS 128, NUMBER = 2*.

*: setup used in our experiments

Notice:

Make sure to compile everytime after modifying any CUDA code

cd submodules/diff-gaussian-rasterization
pip install .

Render

1. Render from training and test views:

python render.py -s data/DATASET_NAME -m output/OUTPUT_NAME  --iteration 3000

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.

2. Render from novel views (add --novel_view):

python render.py -s data/DATASET_NAME -m output/OUTPUT_NAME -f lseg --iteration 3000 --novel_view

(Add numbers after --num_views to change number of views, e.g. --num_views 100, default number is 200)

3. Render from novel views using multiple interpolations (add --novel_view and --multi_interpolate):

python render.py -s data/DATASET_NAME -m output/OUTPUT_NAME -f lseg --iteration 3000 --novel_view --multi_interpolate

Render with editing:

python render.py -s data/DATASET_NAME -m output/OUTPUT_NAME -f lseg --iteration 3000 --edit_config configs/XXX.yaml

Generate videos:

Run to create videos (add --fps to change FPS, e.g. --fps 20 default is 10):

python videos.py --data output/OUTPUT_NAME --fps 10 -f lseg  --iteration 10000 

Inference

LSeg encoder:

Segment from trained model

1. Run the following to segment with 150 labels (default is ADE20K):

python -u segmentation.py --data ../../output/DATASET_NAME/ --iteration 6000

2. Run the following to segment with self-defined label set (e.g. add --label_src car,building,tree):

python -u segmentation.py --data ../../output/DATASET_NAME/ --iteration 6000 --label_src car,building,tree

Calculate segmentaion metric:

cd encoders/lseg_encoder
python -u segmentation_metric.py --backbone clip_vitl16_384 --weights demo_e200.ckpt --widehead --no-scaleinv --student-feature-dir ../../output/OUTPUT_NAME/test/ours_30000/saved_feature/ --teacher-feature-dir ../../data/DATASET_NAME/rgb_feature_langseg/ --test-rgb-dir ../../output/OUTPUT_NAME/test/ours_30000/renders/ --workers 0 --eval-mode test --ground-truth ../../data/DATASET_NAME/label_processed
# If ground tryth label is not provided, --ground_truth is not needed

SAM encoder:

Segment from trained model's embedding with prompt (onnx)

Run with following (add --image to encode features from images):

1. Run with given input point coordinate (e.g. add --point 500 800):

python segment_prompt.py --checkpoint checkpoints/sam_vit_h_4b8939.pth --model-type vit_h --data ../../output/OUTPUT_NAME --iteration 7000 --point 500 800

2. Run with given input box (e.g. add --box 100 100 1500 1200):

python segment_prompt.py --checkpoint checkpoints/sam_vit_h_4b8939.pth --model-type vit_h --data ../../output/OUTPUT_NAME --iteration 7000 --box 100 100 1500 1200

3. Run with given input point (negative) and box (e.g. add --point 500 800 and --box 100 100 1500 1200):

python segment_prompt.py --checkpoint checkpoints/sam_vit_h_4b8939.pth --model-type vit_h --data ../../output/OUTPUT_NAME --iteration 7000 --box 100 100 1500 1200 --point 500 800

(Add--onnx_path to change onnx path)

Segment from trained model's embedding with no prompt (segment entire image)

Run with following (add --image to encode features from images):

python segment.py --checkpoint checkpoints/sam_vit_h_4b8939.pth --model-type vit_h --data ../../output/OUTPUT_NAME --iteration 7000

Timing while segment from trained model's embedding with no prompt

Run with following (remove --feature_path to encode features directly from images):

python segment_time.py --checkpoint checkpoints/sam_vit_h_4b8939.pth --model-type vit_h --image_path ../../output/OUTPUT_NAME/novel_views/ours_7000/renders/ --feature_path ../../output/OUTPUT_NAME/novel_views/ours_7000/saved_feature --output ../../output/OUTPUT_NAME

Acknowledgement

Our repo is developed based on 3D Gaussian Splatting, DFFs and Segment Anything. Many thanks to the authors for opensoucing the codebase.