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
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:
conda create -y -n trisplat python=3.10
conda activate trisplat
pip install --upgrade pip
Install PyTorch and Python dependencies:
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 scripts/env/rebuild_extensions.sh
Download initialization weights used by the model:
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:
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:
data/re10k
data/dl3dv
You can also set:
export RE10K_ROOT="$PWD/data/re10k"
export DL3DV_ROOT="$PWD/data/dl3dv"
Training
Train on RealEstate10K:
bash scripts/train/train_re10k.sh --gpus 0,1,2,3,4,5,6,7 --wandb-mode offline
Train on DL3DV:
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 scripts/eval/eval_re10k_mesh.sh \
--ckpt checkpoints/re10k_trisplat.ckpt \
--data-root "$RE10K_ROOT"
Evaluate and render DL3DV meshes:
bash scripts/eval/eval_dl3dv_mesh.sh \
--ckpt checkpoints/dl3dv_trisplat.ckpt \
--data-root "$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:
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:
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, Open3D, Isaac Sim, Unity, or PyBullet 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:
@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 arXiv:2605.26115},
year={2026}
}
Acknowledgements
This codebase builds on open-source work including YoNoSplat, MVSplat, pixelSplat, CroCo, DINOv2, Omnidata, 3D Gaussian Splatting, and Triangle Splatting.