# UTrice

**Repository Path**: yejun668/UTrice

## Basic Information

- **Project Name**: UTrice
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-01-20
- **Last Updated**: 2026-01-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<h1 align="center">UTrice</h1>

<div align="center">
  <a href="https://arxiv.org/abs/2512.04421">Paper</a>
</div>
<br>

<br>

<div align="center">
  <img src="assets/overview.png" width="800" alt="Abstract Image">
</div>

<br>

This repository is the official implementations of UTrice: Unifying Primitives in Differentiable Ray Tracing and Rasterization via Triangles for Particle-Based 3D Scenes.

We propose a differentiable triangle-based ray tracing pipeline that treats triangles as primitives without proxy geometry, achieving higher rendering quality while maintaining real-time performance. It also directly renders triangles optimized by Triangle Splatting, unifying the primitives used in novel-view-synthesis.

<br>

<div align="center">
  <img src="assets/novel_view.png" width="600" alt="Abstract Image">
</div>


## Requirements

- This project has been built and tested on Ubuntu 22.04 with Python 3.11 and CUDA 12.6.
- To reproduce our results, we recommend a GPU with 24GB VRAM and Ray Tracing (RT) Cores. We tested on an RTX 4500 Ada and RTX 6000 Ada.

## Installation

Clone this repository with submodules: 

```bash
git clone --recursive https://github.com/waizui/UTrice.git
cd UTrice
```

We use Conda to manage the environments. Please install Conda in advance, then execute the following commands to create and activate an environment.

```bash
conda env create -f environment.yaml
conda activate utrice
```

Then install these two modules to finish the installation. 

```bash
pip install submodules/simple-knn --no-build-isolation
pip install submodules/diff-triangle-tracer --no-build-isolation
```

## Training

To train our model, use the following command:
```bash
python train.py -s <path_to_scenes> -m <output_model_path> --eval
```

Add **--outdoor** to train on outdoor scenes:
```bash
python train.py -s <path_to_scenes> -m <output_model_path> --outdoor --eval
```

## Rendering
To render a scene, use:
```bash
python render.py -m <path_to_model>
```

## Evaluation
To evaluate the model, use:
```bash
python metrics.py -m <path_to_model>
```

## Reproduce our results
We provide a convinent script to reproduce the results of our paper, to run this scripts, please
put all scenes inside a sibiling directory of this project and rename it to **datasets**. It should look like:
```bash
<parent>/
 ├─ datasets/
 │   ├─ bicycle/
 │   ├─ garden/
 │   └─ ...
 └─ <UTrice>/
```
Then, run following command:

```bash
python eval_all.py
```

The results will be saved in the **UTrice/output** directory.

## Datasets
The MipNeRF360 scenes are hosted by the paper authors [here](https://jonbarron.info/mipnerf360/). You can find SfM datasets for Tanks&Temples and Deep Blending [here](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/input/tandt_db.zip).

## Render Effects
We provide examples of ray tracing effects using --render_mode: dof, reflect, refract, envlight. 
```bash
python render.py -m <path_to_model> --render_mode <render_mode>
```

<div align="center">
  <img src="assets/effects.png" width="800">
</div>

## Citation
```bibtex
@article{liu2025utrice,
  title = {UTrice: Unifying Primitives in Differentiable Ray Tracing and Rasterization via Triangles for Particle-Based 3D Scenes},
  author = {Changhe Liu and Ehsan Javanmardi and Naren Bao and Alex Orsholits and Manabu Tsukada},
  journal = {arXiv},
  year = {2025},
}
```

```bibtex
@misc{liu2025utriceunifyingprimitivesdifferentiable,
      title={UTrice: Unifying Primitives in Differentiable Ray Tracing and Rasterization via Triangles for Particle-Based 3D Scenes}, 
      author={Changhe Liu and Ehsan Javanmardi and Naren Bao and Alex Orsholits and Manabu Tsukada},
      year={2025},
      eprint={2512.04421},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2512.04421}, 
}
```