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README.md

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 ---
 license: mit
+pipeline_tag: image-to-3d
+tags:
+- 3d
+- novel-view-synthesis
+- triangle-splatting
+- simulation
+datasets:
+- lhmd/re10k_torch
 ---
+
+<h1 align="center">TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction</h1>
+
+<p align="center">
+  <a href="https://lhmd.top/trisplat"><img src="https://img.shields.io/badge/Paper-B31B1B?style=for-the-badge&logo=arxiv&logoColor=white" alt="Paper"></a>
+  <a href="https://lhmd.top/trisplat"><img src="https://img.shields.io/badge/Project%20Page-000000?style=for-the-badge&logo=googlechrome&logoColor=white" alt="Project Page"></a>
+  <a href="https://github.com/ziplab/TriSplat"><img src="https://img.shields.io/badge/Code-181717?style=for-the-badge&logo=github&logoColor=white" alt="Code"></a>
+  <a href="https://huggingface.co/lhmd/TriSplat"><img src="https://img.shields.io/badge/Models-FFD21E?style=for-the-badge&logo=huggingface&logoColor=black" alt="Models"></a>
+</p>
+
+<p align="center">
+  <a href="https://lhmd.top/">Weijie Wang</a><sup>1,*</sup>
+  <a href="https://github.com/puLangMu">Zimu Li</a><sup>1,*</sup>
+  <a href="https://chuan-10.github.io/">Jinchuan Shi</a><sup>1</sup>
+  <a href="https://steve-zeyu-zhang.github.io/">Zeyu Zhang</a><sup>1</sup>
+  <a href="https://botaoye.github.io/">Botao Ye</a><sup>2,3</sup>
+  <a href="https://people.inf.ethz.ch/~pomarc/">Marc Pollefeys</a><sup>2,4</sup>
+  <a href="https://donydchen.github.io/">Donny Y. Chen</a><sup>5</sup>
+  <a href="https://bohanzhuang.github.io/">Bohan Zhuang</a><sup>1</sup>
+</p>
+
+<p align="center">
+  <sup>1</sup>Zhejiang University
+  <sup>2</sup>ETH Zurich
+  <sup>3</sup>ETH AI Center
+  <sup>4</sup>Microsoft
+  <sup>5</sup>Monash University
+</p>
+
+<p align="center">
+  <img src="https://lhmd.top/trisplat/assets/images/teaser.jpg" alt="TriSplat teaser" width="100%">
+</p>
+
+TriSplat is a feed-forward 3D reconstruction model that predicts simulation-ready triangle meshes from sparse, unposed images. Unlike Gaussian-splatting pipelines that require post-hoc mesh extraction, TriSplat directly predicts oriented triangle primitives, camera poses, point maps, and appearance attributes in one forward pass. We train on RealEstate10K and DL3DV, and evaluate zero-shot generalization on ScanNet with RE10K-trained models.
+
+## Method
+
+<p align="center">
+  <img src="https://lhmd.top/trisplat/assets/figures/web/pipeline2.png" alt="TriSplat pipeline" width="100%">
+</p>
+
+Given sparse input views, TriSplat predicts dense local point maps, triangle attributes, camera poses, and optional intrinsics. Point-map geometry anchors triangle orientation through geometry normals, a learned normal refiner, and a monocular-normal bootstrap. A differentiable triangle rasterizer renders RGB, depth, and normals, while mesh export only needs opacity filtering, winding correction, and duplicate-vertex merging.
+
+## Installation
+
+Create the environment:
+
+```bash
+conda create -y -n trisplat python=3.10
+conda activate trisplat
+pip install --upgrade pip
+```
+
+Install PyTorch and Python dependencies:
+
+```bash
+pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 \
+  --index-url https://download.pytorch.org/whl/cu118
+pip install -r requirements.txt --no-build-isolation
+```
+
+Build CUDA extensions:
+
+```bash
+bash scripts/env/rebuild_extensions.sh
+```
+
+Download initialization weights used by the model:
+
+```bash
+mkdir -p pretrained_weights
+wget -O pretrained_weights/pi3.safetensors \
+  https://huggingface.co/yyfz233/Pi3/resolve/main/model.safetensors
+wget -O pretrained_weights/omnidata_dpt_normal_v2.ckpt \
+  'https://zenodo.org/records/10447888/files/omnidata_dpt_normal_v2.ckpt?download=1'
+```
+
+## Models
+
+Download released TriSplat checkpoints from [lhmd/TriSplat](https://huggingface.co/lhmd/TriSplat):
+
+```bash
+mkdir -p checkpoints
+wget -O checkpoints/re10k_trisplat.ckpt \
+  https://huggingface.co/lhmd/TriSplat/resolve/main/re10k_trisplat.ckpt
+wget -O checkpoints/dl3dv_trisplat.ckpt \
+  https://huggingface.co/lhmd/TriSplat/resolve/main/dl3dv_trisplat.ckpt
+```
+
+## Datasets
+
+Packed `.torch` datasets default to:
+
+```text
+data/re10k
+data/dl3dv
+```
+
+You can also set:
+
+```bash
+export RE10K_ROOT="$PWD/data/re10k"
+export DL3DV_ROOT="$PWD/data/dl3dv"
+```
+
+## Training
+
+Train on RealEstate10K:
+
+```bash
+bash scripts/train/train_re10k.sh --gpus 0,1,2,3,4,5,6,7 --wandb-mode offline
+```
+
+Train on DL3DV:
+
+```bash
+bash scripts/train/train_dl3dv.sh --gpus 0,1,2,3,4,5,6,7 --wandb-mode offline
+```
+
+Extra arguments after `--` are passed to Hydra. Use `--ckpt` to resume or initialize from a checkpoint.
+
+## Evaluation
+
+Evaluate and render RealEstate10K meshes:
+
+```bash
+bash scripts/eval/eval_re10k_mesh.sh \
+  --ckpt checkpoints/re10k_trisplat.ckpt \
+  --data-root "$RE10K_ROOT"
+```
+
+Evaluate and render DL3DV meshes:
+
+```bash
+bash scripts/eval/eval_dl3dv_mesh.sh \
+  --ckpt checkpoints/dl3dv_trisplat.ckpt \
+  --data-root "$DL3DV_ROOT"
+```
+
+
+## Simulation
+
+TriSplat exports ordinary triangle meshes, so the output can be opened directly by common graphics and simulation tools. The evaluation scripts above write per-scene meshes under:
+
+```text
+outputs/<eval_root>/<run_name>/<scene>/mesh/DIRECT_triangle_mesh.ply
+outputs/<eval_root>/<run_name>/<scene>/mesh/DIRECT_triangle_mesh.off
+outputs/<eval_root>/<run_name>/<scene>/mesh/DIRECT_triangle_mesh_post.ply
+outputs/<eval_root>/<run_name>/<scene>/mesh/DIRECT_triangle_mesh_post.off
+```
+
+The `_post` mesh is the default rendering and simulation output. It applies connected-component cleanup to the direct mesh, keeping the largest components and removing small disconnected floaters, unreferenced vertices, and degenerate triangles. For example, after running `scripts/eval/eval_re10k_mesh.sh`, use:
+
+```bash
+ls outputs/re10k_mesh_eval/re10k_mesh_eval/*/mesh/DIRECT_triangle_mesh_post.ply
+```
+
+The exported `_post.ply` mesh is vertex-colored and can be imported into [Blender](https://www.blender.org/), [Open3D](https://www.open3d.org/), [Isaac Sim](https://developer.nvidia.com/isaac/sim), [Unity](https://unity.com/), or [PyBullet](https://pybullet.org/) as a static triangle mesh. For simulation, use the `.ply` mesh for visual geometry and generate a collision mesh in your simulator if needed; for example, simplify or convex-decompose it before rigid-body simulation when the raw mesh is too dense.
+
+## Citation
+
+If you find this repository useful, please cite:
+
+```bibtex
+@article{wang2026trisplat,
+  title={TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction},
+  author={Wang, Weijie and Li, Zimu and Shi, Jinchuan and Zhang, Zeyu and Ye, Botao and Pollefeys, Marc and Chen, Donny Y. and Zhuang, Bohan},
+  journal={arXiv preprint},
+  year={2026}
+}
+```
+
+## Acknowledgements
+
+This codebase builds on open-source work including [YoNoSplat](https://github.com/justimyhxu/YoNoSplat), [MVSplat](https://github.com/donydchen/mvsplat), [pixelSplat](https://github.com/dcharatan/pixelsplat), [CroCo](https://github.com/naver/croco), [DINOv2](https://github.com/facebookresearch/dinov2), [Omnidata](https://github.com/EPFL-VILAB/omnidata), [3D Gaussian Splatting](https://github.com/graphdeco-inria/gaussian-splatting), and [Triangle Splatting](https://github.com/trianglesplatting/triangle-splatting).