ConeGS: Error-Guided Densification Using Pixel Cones for Improved Reconstruction with Fewer Primitives

Bartłomiej Baranowski · Stefano Esposito · Patricia Gschoßmann · Anpei Chen · Andreas Geiger

🛠️ Installation

The code was tested on Python 3.11 with PyTorch 2.5.1 with CUDA Toolkit 12.1 and 11.8.

Installed CUDA Toolkit is required. To run the code install the following packages:

git clone https://github.com/baranowskibrt/conegs.git --recursive

conda create -n conegs python=3.11
conda install pytorch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 pytorch-cuda=12.1 -c pytorch -c nvidia
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
pip install git+https://github.com/nerfstudio-project/nerfacc.git
pip install submodules/simple-knn
pip install submodules/diff-gaussian-rasterization

pip install -r requirements.txt

📝 Usage

Single scene

For a particular scene run (Mip-NeRF360's bicycle scene as an example) with a specified budget run:

python train.py --config-name defaults.yaml gaussian_model.source_path=../scenes_mipnerf scene_name=bicycle run_name=benchmark optimization.max_points=100000 gaussian_model.images=images_4 

And without budget:

python train.py --config-name defaults.yaml gaussian_model.source_path=../scenes_mipnerf scene_name=bicycle run_name=benchmark optimization.max_points=100000 gaussian_model.images=images_4 

Benchmark

For all benchmarks on a specific budget adjust the max points (primitives), and run:

python full_eval.py --mipnerf360 ../scenes_mipnerf --tanksandtemples ../scenes_tt --deepblending ../scenes_db --ommo ../scenes --output_path benchmarks --common_args " optimization.max_points=100000"

Or for unbudget case:

python full_eval.py --mipnerf360 ../scenes/ --tanksandtemples ../scenes/ --deepblending ../scenes --ommo ../scenes --output_path benchmarks --common_args " optimization.max_points=0 optimization.gaussian_percentage_increase=0.02"

🔄 Pipeline

🛠️ Data

Datasets

You can access the datasets we evaluated on here:

• MipNeRF360
• OMMO
• Tanks & Temples and Deep Blending

Your own data

Check out the 3DGS code for the preprocessing needed.

🎓 Citation

@inproceedings{baranowski2026conegs, 
    title={ConeGS: Error-Guided Densification Using Pixel Cones for Improved Reconstruction with Fewer Primitives}, 
    author={Bartłomiej Baranowski and Stefano Esposito and Patricia Gschoßmann and Anpei Chen and Andreas Geiger},
    year={2026},
    booktitle = {2026 International Conference on 3D Vision (3DV)}, 
}