--- license: mit pipeline_tag: image-to-3d tags: - 3d - novel-view-synthesis - triangle-splatting - simulation datasets: - lhmd/re10k_torch ---

TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction

Weijie Wang^1,* Zimu Li^1,* Jinchuan Shi¹ Zeyu Zhang¹ Botao Ye^2,3
Marc Pollefeys^2,4 Donny Y. Chen⁵ Bohan Zhuang¹

¹Zhejiang University ²ETH Zurich ³ETH AI Center ⁴Microsoft ⁵Monash University

TriSplat teaser

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

TriSplat pipeline

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////mesh/DIRECT_triangle_mesh.ply outputs////mesh/DIRECT_triangle_mesh.off outputs////mesh/DIRECT_triangle_mesh_post.ply outputs////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 arXiv:2605.26115}, 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).