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DN-Splatter + AGS-Mesh

This repo implements research papers (DN-Splatter and AGS-Mesh) for depth and normal supervision of Gaussian splatting models for improved novel-view synthesis using smartphone data (iPhones) and mesh reconstruction.

Pipelines:

Quick demo:

https://github.com/maturk/dn-splatter/assets/30566358/9b3ffe9d-5fe9-4b8c-8426-d578bf877a35

Updates

• 29.11.2024: We release AGS-Mesh which improves mesh reconstruction using a novel depth and normal filtering strategy, and octree-based isosurface extraction method. We release code for Splatfacto based AGS-Mesh model and 2DGS based AGS-Mesh model. We release AGS-Mesh plug in code for both DN-Splatter and Inria 2DGS version. Please switch to AGS-Mesh-2dgs for Inria 2DGS version.
• 17.10.2024: FusionSense improves DN-Splatter in sparse settings for robotic tactile applications! 🚀
• 04.09.2024: Support Open3d TSDF to extract mesh, support Patch-based Depth Correlation Loss from SparseGS for monodepth supervision, support visualizing normal estimates from the Gaussian geometry and estimated surface normal from depths, support colmap SFM point cloud initialization for MuSHRoom dataset.
• 14.06.2024: Support gsplat v1.0.0 🚀. Faster training and better memory consumption. Training with --pipeline.model.predict_normals is about 20% slower than without.
• 16.04.2024: Support for DSINE monocular normal supervision.
• 20.04.2024: dn-splatter-big: a variant featuring less aggressive Gaussian culling threshold, which leads to an increased number of Gaussians in the optimized scene. On certain datasets, this can lead to better novel-view synthesis results.

Installation

Clone and install DN-Splatter
```bash
conda activate nerfstudio
git clone https://github.com/maturk/dn-splatter
cd dn_splatter/
pip install setuptools==69.5.1
pip install -e .
```

Clone and install DN-Splatter

git clone https://github.com/maturk/dn-splatter
cd dn_splatter/
pixi install

To run an example

pixi run example

To activate conda enviroment

pixi shell

Data Preparation Scripts

We support generating low and hd monocular normal estimates from a pretrained omnimodel and from DSINE.

1. Omnidata normals:

You need to download the model weights first:

python dn_splatter/data/download_scripts/download_omnidata.py

Then generate normal maps using the following command:

python dn_splatter/scripts/normals_from_pretrain.py
        --data-dir (path to data root which either has a transforms.json or images/ folder)
        --img-dir-name (if transforms.json file is not found, use images in this folder (default /images))
        --resolution {low, hd} (low default and reccommended)

We highly reccommend using low res normal maps, since generating HD versions from omnidata (that match the dataset image size) is very time consuming.

2. DSINE normals:

To generate normals from DSINE, run the following command:

python dn_splatter/scripts/normals_from_pretrain.py --data-dir [PATH_TO_DATA] --model-type dsine

If using DSINE normals for supervision, remember to use the --normal-format opencv in your ns-train command. An example command is as follows:

ns-train dn-splatter --pipeline.model.use-normal-loss True --pipeline.model.normal-supervision mono replica --data ./datasets/Replica/ --normals-from pretrained --normal-format opencv

Important notes:

Default save path of generated normals is data_root/normals_from_pretrain And to enable training with pretrained normals, add --normals-from pretrained flag in the dataparser.

NOTE: different monocular networks can use varying camera coordinate systems for saving/visualizing predicted normals in the camera frame. We support both OpenGL and OpenCV coordinate systems. Each dataparser has a flag --normal-format [opengl/opencv] to distinguish between them. We render normals into the camera frame according to OpenCV color coding which is similar to Open3D. Some software might have different conventions. Omnidata normals are stored in OpenGL coordinates, but we convert them to OpenCV for consistency across the repo.

If your dataset has no camera pose information, you can generate poses using COLMAP.

Convert a dataset of images to COLMAP format with

python dn_splatter/scripts/convert_colmap.py --image-path [data_root/images] --use-gpu/--no-use-gpu

If your dataset has no sensor depths, and you have a COLMAP processed dataset, we provide a script to generate scale aligned monocular depth estimates. Scale alignment refers to solving for the scale ambiquity between estimated monocular depths and the scale of your input COLMAP poses.

This script generates sfm_depths/ and mono_depth/ directories in the data root:

<data>
|---image_path
|   |---<image 0>
|   |---<image 1>
|   |---...
|---sfm_depths
|   |---<sfm_depth 0>
|   |---<sfm_depth 1>
|   |---...
|---mono_depth
|   |---<mono_depth 0>.png
|   |---<mono_depth 0>_aligned.npy

The dataset is expected to be in COLMAP format (contains a colmap/sparse/0 folder in data root) since SfM points are required.

python dn_splatter/scripts/align_depth.py --data [path_to_data_root] \
                                      --skip-colmap-to-depths, --no-skip-colmap-to-depths \
                                      --skip-mono-depth-creation, --no-skip-mono-depth-creation \

NOTE: if faced with the following error:

TypeError: expected size to be one of int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int], but got size with types [<class 'numpy.int64'>, <class 'numpy.int64'>]

Downgrading from Torch 2.1.2 to 2.0.1 solves the issue.

To skip SfM alignment and just render monocular depths for your dataset, use the following script:

python dn_splatter/scripts/align_depth.py --data [path_to_data_root] \
                                      --skip-colmap-to-depths  --skip_alignment \
                                      

To generate the depth mask to run AGS-Mesh, using:

python depth_normal_consistency.py --data_dir <dataset_path> --transform_name <transformation script.json>                                    

Usage

This repo registers a model called dn-splatter and ags-mesh with various additional options:

Please check the dn_model.py for a full list of supported configs (some are only experimental).

Model overviews

dn-splatter is the baseline model that implements depth and normal supervision within 3DGS. ags-mesh improves dn-splatter with a novel depth and normal filtering strategy. Please see dn-splatter and ags-mesh research papers for more details regarding model architectures. We provide a 2DGS based version of AGS-Mesh in the ags-mesh-2dgs branch.

Recommended settings:

For larger indoor captures with sensor depth data (e.g. MuSHRoom / ScanNet++ datasets):

# to run the dn-splatter model
ns-train dn-splatter --data PATH_TO_DATA \
                 --pipeline.model.use-depth-loss True \
                 --pipeline.model.depth-lambda 0.2 \
                 --pipeline.model.use-normal-loss True \
                 --pipeline.model.use-normal-tv-loss True \
                 --pipeline.model.normal-supervision (mono/depth)
# to run the ags-mesh model
ns-train ags-mesh --data PATH_TO_DATA \
                 --pipeline.model.use-depth-loss True \
                 --pipeline.model.depth-lambda 0.2 \
                 --pipeline.model.use-normal-loss True \
                 --pipeline.model.normal-supervision (mono/depth)

dn-splatter-big:

We also provide a dn-splatter-big variant that increases the number of Gaussians in the scene which may enhance the quality of novel-view synthesis. This increases training time and hardware requirements. Simply replace the dn-splatter keyword with dn-splatter-big in the above commands.

Supported Depth Losses

To train with a specific depth loss, use the flag: --pipeline.model.depth-loss-type DepthLossType where DepthLossType is one of ["MSE", "LogL1", "HuberL1", "L1", "EdgeAwareLogL1", "PearsonDepth"]

For sensor depth supervision we reccommend EdgeAwareLogL1 loss. For monocular depth supervision, we recommend the relative Pearson correlation loss PearsonDepth.

Mesh

To extract a mesh, run the following command:

gs-mesh {dn, tsdf, o3dtsdf, sugar-coarse, gaussians, marching} --load-config [PATH] --output-dir [PATH]

We reccommend using gs-mesh o3dtsdf.

Export a mesh with the gs-mesh --help command. The following mesh exporters are supported.

Use the --help command with each method to see more useful options and settings.

For very small object captures, TSDF works well with 0.004 voxel size and 0.02 SDF trunc distance.

Custom RGB-D Smartphone (Android/iPhone) Data

For casually captured RGB-D streams, consider using SpectacularAI SDK for iPhone/Android or Oak/RealSense/Kinect sensor streams. For LiDaR enabled smartphones, download the app from the Apple/Play store and capture your data.

Once you have gathered the data, process the inputs into a Nerfstudio suitable format (calculate VIO poses and create a transforms.json file with poses and depth frames):

pip install spectacularAI
python dn_splatter/scripts/process_sai.py [PATH_TO_SAI_INPUT_FOLDER] [PATH_TO_OUTPUT_FOLDER]

To train with the custom data:

ns-train dn-splatter --data PATH_TO_DATA \
                 --pipeline.model.use-depth-loss True \
                 --pipeline.model.depth-lambda 0.2 \
                 --pipeline.model.use-normal-loss True \
                 --pipeline.model.use-normal-tv-loss True \
                 --pipeline.model.normal-supervision depth \

For other custom datasets, use the Nerfstudio conventions and train with the above command.

Datasets

Other preprocessed datasets are supported by dataparsers with the keywords mushroom, replica, scannetpp, nrgbd, dtu, coolermap. To train with a dataset use the following:

ns-train dn-splatter [OPTIONS] [mushroom/replica/scannet/nrgbd/dtu/coolermap] --data [DATASET_PATH]

Dataparsers have their own options, to see the full list use ns-train dn-splatter [mushroom/replica/scannet/nrgbd/dtu/coolermap] --help. Some useful ones are:

  --depth-mode        : ["sensor","mono","all", "none"] determines what depths to load.
  --load-normals      : [True/False] whether to load normals or not.
  --normals-from      : ["depth", "pretrained"] generate pseudo-ground truth normals from depth maps or from pretrained omnimodel.
  --normal-format     : ["opengl", "opencv"] What coordinate system normals are saved in camera frame.
  --load-pcd-normals  : [True/False] initialise gaussian scales/rotations based on estimated SfM normals.

Supported dataparsers:

<base_dir>
|---image_path
|   |---<image 0>
|   |---<image 1>
|   |---...
|---colmap
    |---sparse
        |---0
            |---cameras.bin
            |---images.bin
            |---points3D.bin

Use the coolermap dataparser with COLMAP datasets as follows:

ns-train dn-splatter [OPTIONS] coolermap --data [DATASET_PATH]

Support for Kinect and iPhone RGB-D trajectories.

Download per-room datasets with python dn_splatter/data/download_scripts/mushroom_download.py --room-name [name]

(OPTIONAL) Download Faro scanner reference depths with python dn_splatter/data/mushroom_utils/reference_depth_download.py

Use the mushroom dataparser as follows:

ns-train dn-splatter \
--pipeline.model.use-depth-loss True \
--pipeline.model.depth-lambda 0.2 \
--pipeline.model.use-normal-loss True \
--pipeline.model.normal-supervision (mono/depth) \
mushroom --data [DATASET_PATH] --mode [kinect/iphone]

To get MuSHRoom iPhone sequence with colmap SFM initialized point cloud:

python dn_splatter/scripts/poses_to_colmap_sfm.py --transforms_path [path/transformations_colmap.json] --run_colmap

ns-train dn-splatter \
--pipeline.model.use-depth-loss True \
--pipeline.model.depth-lambda 0.2 \
--pipeline.model.use-normal-loss True \
--pipeline.model.use-normal-tv-loss True \
--pipeline.model.normal-supervision (mono/depth) \
mushroom --data [DATASET_PATH] --mode iphone --create_pc_from_colmap True

For easy use, we provide the result of the converted COLMAP pose and point cloud in zenodo.

Download the dataset with python dn_splatter/data/download_scripts/replica_download.py

Use the replica dataparser as follows:

ns-train dn-splatter 
--pipeline.model.use-depth-loss True \
--pipeline.model.depth-lambda 0.5 \
--pipeline.model.use-normal-loss True \
--pipeline.model.use-normal-tv-loss True \
--pipeline.model.normal-supervision (mono/depth) \
replica --data [DATASET_PATH] --sequence [office0/office1/office2/office3/office4/room0/room1/room2] 

We use the following sequences:

8b5caf3398
b20a261fdf

First process the sequences according to the <a href="https://github.com/scannetpp/scannetpp">ScanNet++ toolkit</a>:

Extract the undistorted images with:

python -m dslr.undistort_colmap dslr/configs/undistort_colmap.yml

and extract iphone rgb, mask and depth frames with:

python -m iphone.prepare_iphone_data iphone/configs/prepare_iphone_data.yml

Use the scannetpp dataparser as follows

ns-train dn-splatter  \
--pipeline.model.use-depth-loss True \
--pipeline.model.depth-lambda 0.2 \
--pipeline.model.use-normal-loss True \
--pipeline.model.use-normal-tv-loss True \ 
scannetpp --sequence [8b5caf3398/b20a261fdf] --data [DATASET_PATH] 

Download with python dn_splatter/data/download_scripts/nrgbd_download.py

ns-train dn-splatter [OPTIONS] nrgbd --sequence whiteroom

Download with python dn_splatter/data/download_scripts/dtu_download.py

ns-train dn-splatter gsdf --sequence scan65 --data [DATASET_PATH] 

First download the <a href="https://drive.google.com/file/d/0B-ePgl6HF260UXlhWDBiNVZvdk0/view?usp=sharing&resourcekey=0-eliRKXsZ8_vZ7KELO7oPgQ">advanced scenes</a> from the official website.

We extract colmap poses with from the following

 python dn_splatter/scripts/convert_colmap.py --image-path [path_to_image_folder] --use-gpu / --no-use-gpu

ns-train dn-splatter [OPTIONS] coolermap --data [DATASET_PATH] 

Evaluation

Please see dn_splatter/eval/eval_instructions.md for more details.

To run DN-Splatter on an entire dataset of sequences (potentially in parallel with multi-GPU cluster), you can use the dn_splatter/eval/batch_run.py script.

For evaluating rgb, depth, and pointcloud metrics (optional), run the following command:

ns-eval --load-config [PATH_TO_CONFIG] --output-path [JSON_OUTPUT_PATH]

To render train/eval images also add the flag --render-output-path [PATH_TO_IMAGES]

To get mesh metrics for the MuSHRoom dataset, run the following command:

python dn_splatter/eval/eval_mesh_mushroom_vis_cull.py --gt_mesh_path [GT_Mesh_Path] --pred_mesh_path [Pred_Mesh_Path] --output --device [iphone/kinect] 

To get mesh metrics for other datasets ScanNet++/Replica or custom datasets, run the following command:

python dn_splatter/eval/eval_mesh_vis_cull.py --gt-mesh-path [GT_Mesh_Path] --pred-mesh-path [Pred_Mesh_Path] --transformation_file [Path_to_transform_file] --dataset_path [Dataset path]

--transformation_file path is the path of the transform.json generated when training scannetpp here. --dataset_path is the path to the dataset folder, e.g. datasets/scannetpp/data/b20a261fdf/iphone

Acknowledgements

I want to thank Tobias Fischer, Songyou Peng and Philipp Lindenberger for their fruitful discussions and guidance, especially concerning mesh reconstruction. This project is built on various open-source software, and I want to thank the Nerfstudio team for their great efforts maintaining and extending a large project allowing for these kinds of extensions to exist.

Citation

If you find this work useful in your research, consider citing DN-Splatter:

@InProceedings{turkulainen2024dnsplatter,
        title={DN-Splatter: Depth and Normal Priors for Gaussian Splatting and Meshing}, 
        author={Matias Turkulainen and Xuqian Ren and Iaroslav Melekhov and Otto Seiskari and Esa Rahtu and Juho Kannala},
        booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
        year={2025}
}

and AGS-Mesh:

@InProceedings{ren2024agsmesh,
        title={AGS-Mesh: Adaptive Gaussian Splatting and Meshing with Geometric Priors for Indoor Room Reconstruction Using Smartphones}, 
        author={Xuqian Ren and Matias Turkulainen and Jiepeng Wang and Otto Seiskari and Iaroslav Melekhov and Juho Kannala and Esa Rahtu},
        booktitle={International Conference on 3D Vision (3DV)}
        year={2025},
        archiveprefix={arxiv},
        eprint={2411.19271},
}

Contributing

We welcome any bug fixes, issues/comments, or contributions. Also, if you have any suggestions or improvements, let me know!

I want to thank Pablo Vela for contributing the DSINE normal predictor into this project and allowing for an alternative (much easier) installation with Pixi.

Developers

• maturk
• XuqianRen