---
title: CodecSplat
emoji: 🗜️
colorFrom: red
colorTo: yellow
sdk: gradio
sdk_version: 6.30.0
app_file: app.py
short_description: Compress a 3D Gaussian scene into a few KB
python_version: "3.12"
startup_duration_timeout: 1h
models:
  - pengpeng-yu/CodecSplat
---

# CodecSplat: Ultra-Compact Latent Coding for Feed-Forward 3D Gaussian Splatting

Interactive demo of [CodecSplat](https://huggingface.co/papers/2605.25563)
([code](https://github.com/pengpeng-yu/CodecSplat), MIT;
[weights](https://huggingface.co/pengpeng-yu/CodecSplat)).

Upload overlapping photos of a static scene. The Space:

1. runs **COLMAP** (`pycolmap`, CPU) to estimate intrinsics and camera poses,
2. picks 8 context views by farthest-point sampling and resizes them to 256x448
   (the `codecsplat-base-dl3dv-256x448-view8` checkpoint),
3. runs the authors' **complete bitstream path** (`EncoderCodecSplat.codec_forward`):
   it encodes the latent features into a real entropy-coded scene bitstream at the
   chosen rate point λ (16-1024), then decodes that bitstream back to 3D Gaussians,
4. renders held-out views (with PSNR/SSIM against the real photos) and a
   fly-through with `gsplat`, and shows the Gaussians in an interactive viewer.

You can download both the `.bin` bitstream and the decoded `.ply`.

## Implementation notes

* Model construction uses the exact Hydra overrides of the authors'
  `scripts/test_dl3dv_view8_256x448.sh` (bf16 autocast, gsplat antialiased rasterizer).
* COLMAP stage and scene loader are adapted from the
  [ReSplat Space](https://huggingface.co/spaces/hugging-apps/resplat-recurrent-gs) / the ReSplat
  reference `scripts/infer_colmap.py` (same DepthSplat camera conventions). The reconstruction is
  normalised to a fixed extent so that the DL3DV near/far planes (0.5 / 200) are meaningful.
* `gsplat`'s CUDA kernels are JIT-compiled once at startup (no prebuilt sm_120 wheel).

## Example assets

`examples/kitchen` and `examples/garden` are 14 frames each from the
[Mip-NeRF 360](https://jonbarron.info/mipnerf360/) dataset (Barron et al.), downscaled to 960px,
as redistributed by [`nvs-bench/mipnerf360`](https://huggingface.co/datasets/nvs-bench/mipnerf360) (MIT).