LoCoSplat: Real-Time Feed-Forward 3D Gaussian Splatting with Minimal 3D Reasoning
Sinan Wang*, Jinjin He*, Yuchen Sun, Duowen Chen, Shenyifan Lu, Bo Zhu (Georgia Institute of Technology; * equal contribution)
Paper (arXiv:2610.04351) · Code
LoCoSplat is a feed-forward 3D Gaussian Splatting model that replaces the learned 3D network of voxel-aligned methods with two fixed grids (voxel 0.001 and 0.128) read back at each anchor by a 0.14M-parameter pointwise MLP. With no learned 3D network and no dynamic sparse computation, the whole encoder runs as one fp16 CUDA graph: a 6-view scene is reconstructed in 33 ms on one RTX PRO 6000.
Checkpoints
All trained on RealEstate10K (256x256) for 150k steps, 4 GPUs x batch 1, with VolSplat's official
recipe (config/experiment/locosplat_re10k.yaml in the code repository). Weights only
(state_dict, optimizer states stripped), 397 MB each; 99.1 M parameters of which 86.6 M are the
frozen Depth Anything V2 ViT-B backbone.
| file | trained with | RE10K official index: PSNR / SSIM / LPIPS | sha256 |
|---|---|---|---|
locosplat_re10k_6v.ckpt | 6 context views | 31.51 / 0.942 / 0.074 | 03b03e4531167a65d8caa03dcf905212aaa4a2df408b3d4af6bd6d9adeb0485b |
locosplat_re10k_12v.ckpt | 12 context views | 31.47 / 0.942 / 0.073 | fffd4dc768fc047a95321078be2dd4b6580390dd728720dd0020438b8387ed76 |
locosplat_re10k_24v.ckpt | 24 context views | 30.57 / 0.934 / 0.082 | 790ce657dfe56a04a45a13fae4fee1528829eccef561b087d3cad2893a832e80 |
The 6-view checkpoint is also the model evaluated zero-shot on ACID (33.16 / 0.931 / 0.091) and the initialisation of the ScanNet fine-tuning in the paper.
Usage
git clone https://github.com/swang3081/LoCoSplat && cd LoCoSplat # install: see README
hf download swang3081/LoCoSplat --local-dir models
scripts/eval_re10k.sh 6 models/locosplat_re10k_6v.ckpt # plain fp32
FAST=1 scripts/eval_re10k.sh 6 models/locosplat_re10k_6v.ckpt # deployed fp16 + CUDA-graph path
scripts/eval_re10k.sh 12 models/locosplat_re10k_12v.ckpt
scripts/eval_re10k.sh 24 models/locosplat_re10k_24v.ckpt
Each checkpoint file carries a locosplat dict with the training view count and the exact hydra
overrides of its run.
Citation
@article{wang2026locosplat,
title = {LoCoSplat: Real-Time Feed-Forward 3D Gaussian Splatting with Minimal 3D Reasoning},
author = {Wang, Sinan and He, Jinjin and Sun, Yuchen and Chen, Duowen and Lu, Shenyifan and Zhu, Bo},
journal = {arXiv preprint arXiv:2610.04351},
year = {2026}
}
Acknowledgements
Built on VolSplat, DepthSplat, UniMatch and Depth Anything V2.