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LICENSE

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+MIT License
+
+Copyright (c) 2026 Pengpeng Yu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+license: mit
+tags:
+- 3d-gaussian-splatting
+- novel-view-synthesis
+- compression
+- feed-forward
+---
+
+<p align="center">
+  <h1 align="center">CodecSplat: Ultra-Compact Latent Coding for Feed-Forward 3D Gaussian Splatting</h1>
+  <h3 align="center">
+    <a href="https://arxiv.org/abs/2605.25563">Paper</a> |
+    <a href="https://github.com/pengpeng-yu/CodecSplat">Code</a> |
+    <a href="https://huggingface.co/pengpeng-yu/CodecSplat">Models</a>
+  </h3>
+</p>
+
+CodecSplat is a latent coding framework for feed-forward 3D Gaussian splatting.
+Instead of compressing the final irregular 3D Gaussian primitives, it
+entropy-codes an intermediate 2D feature representation used for depth and
+Gaussian prediction. This keeps the scene representation compact while
+preserving fast feed-forward reconstruction.
+
+## Installation
+
+The code is developed with Python 3.10 and PyTorch 2.9.1. Please refer to the official
+[PyTorch installation guide](https://pytorch.org/get-started/locally/) if your
+CUDA version or platform differs from the example below.
+
+```bash
+conda create -y -n py310torch291 python=3.10
+conda activate py310torch291
+
+pip install torch==2.9.1 torchvision==0.24.1 torchaudio==2.9.1 \
+  --index-url https://download.pytorch.org/whl/cu130
+pip install -r requirements.txt
+```
+
+## Model Zoo
+
+Pretrained weights are available on
+[Hugging Face](https://huggingface.co/pengpeng-yu/CodecSplat):
+
+| Checkpoint | Dataset | Input views | Resolution | Notes |
+| --- | --- | ---: | --- | --- |
+| [CodecSplat-Re10K-2view](https://huggingface.co/pengpeng-yu/CodecSplat/resolve/main/codecsplat-base-re10k-256x256-view2-e3a545db.pth) | RealEstate10K | 2 | 256x256 | Variable-rate, lmb=16-1024 |
+| [CodecSplat-DL3DV-8view](https://huggingface.co/pengpeng-yu/CodecSplat/resolve/main/codecsplat-base-dl3dv-256x448-view8-075062d0.pth) | DL3DV | 8 | 256x448 | Variable-rate, lmb=16-1024 |
+
+After downloading a checkpoint, place it under `pretrained/` or update the
+`checkpointing.pretrained_model=...` entry in the corresponding test script.
+
+## Datasets
+
+CodecSplat is evaluated on 256x256 RealEstate10K and 256x448 DL3DV. The
+datasets used by the provided scripts are available from:
+
+- [RealEstate10K 360p torch format](https://huggingface.co/datasets/lhmd/re10k_torch)
+- [DL3DV-ALL-480P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-480P)
+- [DL3DV-Benchmark](https://huggingface.co/datasets/DL3DV/DL3DV-Benchmark)
+
+The RealEstate10K release above is already in the expected torch-chunk format.
+For DL3DV, convert the downloaded source data with:
+
+```bash
+mkdir -p datasets
+
+# DL3DV benchmark/test split.
+python src/scripts/convert_dl3dv.py \
+  --split test \
+  --input_dir /path/to/DL3DV-Benchmark \
+  --output_dir datasets/DL3DV-Benchmark-480P-Processed \
+  --img_subdir images_8
+
+# DL3DV training split. Test scenes are excluded by index.
+python src/scripts/convert_dl3dv.py \
+  --split train \
+  --input_dir /path/to/DL3DV-ALL-480P \
+  --output_dir datasets/DL3DV-ALL-480P-Processed \
+  --img_subdir images_8 \
+  --test_index_path datasets/DL3DV-Benchmark-480P-Processed/test/index.json
+
+# Use the DL3DV benchmark split as the test split for DL3DV evaluation.
+ln -s ../DL3DV-Benchmark-480P-Processed/test datasets/DL3DV-ALL-480P-Processed/test
+```
+
+The resulting layout should look like:
+
+```text
+datasets
+├── DL3DV-ALL-480P
+│   ├── 1K
+│   │   ├── 0a1b7c20...
+│   │   │   ├── images_8  # 270x480
+│   │   │   └── transforms.json
+│   │   └── ...
+├── DL3DV-Benchmark
+│   ├── 0a1b7c20...
+│   │   ├── images_8  # 270x480
+│   │   └── transforms.json
+│   └── ...
+├── RE10K-Torch-360p
+│   ├── train
+│   │   ├── 000000.torch
+│   │   ├── ...
+│   │   └── index.json
+│   └── test
+│       ├── 000000.torch
+│       ├── ...
+│       └── index.json
+├── DL3DV-ALL-480P-Processed
+│   ├── train
+│   │   ├── 000000.torch
+│   │   ├── ...
+│   │   └── index.json
+│   └── test -> ../DL3DV-Benchmark-480P-Processed/test
+└── DL3DV-Benchmark-480P-Processed
+    └── test
+        ├── 000000.torch
+        ├── ...
+        └── index.json
+```
+
+## Evaluation
+
+The main codec evaluation entry point is:
+
+```bash
+# Run both datasets at the default rate points, lmb=16 and lmb=1024.
+python scripts/test_codec_bitstream_rd.py all
+
+# Run one dataset at selected rate points.
+python scripts/test_codec_bitstream_rd.py dl3dv 16 128 1024
+python scripts/test_codec_bitstream_rd.py re10k 16 128 1024
+```
+
+Equivalent single-dataset shell scripts are also provided:
+
+```bash
+# DL3DV 8-view 256x448 evaluation.
+lmb=1024 bash scripts/test_dl3dv_view8_256x448.sh
+
+# RealEstate10K 2-view 256x256 evaluation.
+lmb=1024 bash scripts/test_re10k_view2_256x256.sh
+```
+
+Useful test flags:
+
+- `test.save_image=true`: save rendered target views.
+- `test.save_gt_image=true`: save ground-truth target views.
+- `test.save_input_images=true`: save context/input views.
+- `test.save_depth=true`: save predicted depths.
+- `test.save_gaussian=true`: save reconstructed Gaussian primitives as PLY.
+- `test.save_video=true`: save rendered target-view sequences as MP4 when the
+  evaluation index contains consecutive frames.
+- `model.encoder.codec_lmb_range=[L,L]`: evaluate a fixed rate point L.
+- `model.encoder.codec_eval_use_bitstream=true`: run actual compress/decompress bitstream evaluation.
+
+## Video Rendering
+
+CodecSplat supports video rendering through `test.save_video=true`.
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python -m src.main +experiment=dl3dv \
+mode=test \
+dataset/view_sampler=evaluation \
+dataset.view_sampler.num_context_views=12 \
+dataset.view_sampler.index_path=assets/dl3dv_start_0_distance_100_ctx_12v_video.json \
+dataset.roots=[datasets/DL3DV-Benchmark-480P-Processed] \
+dataset.image_shape=[256,448] \
+dataset.test_len=1 \
+test.save_video=true \
+test.compute_scores=false \
+test.render_chunk_size=10 \
+test.stabilize_camera=true \
+model.encoder.codec_lmb_range=[1024.0,1024.0] \
+model.encoder.codec_eval_use_bitstream=true \
+checkpointing.pretrained_model=/path/to/dl3dv_codec_checkpoint.ckpt \
+checkpointing.no_strict_load=true \
+output_dir=outputs/codecsplat_video_smoke
+```
+
+## Training
+
+Training is performed in two stages.
+
+Stage 1 trains the feed-forward Gaussian reconstruction model without the learned
+feature codec. Stage 2 enables the feature codec and trains the compression path.
+
+```bash
+# DL3DV 8-view, 256x448.
+bash scripts/train_dl3dv_view8_256x448_stage1.sh
+bash scripts/train_dl3dv_view8_256x448_stage2.sh
+
+# RealEstate10K 2-view, 256x256.
+bash scripts/train_re10k_view2_256x256_stage1.sh
+bash scripts/train_re10k_view2_256x256_stage2.sh
+```
+
+The comments in the training scripts use notation such as `1 x 8 GPUs` or
+`4 x 8 GPUs`. This means per-GPU batch size times the number of GPUs. In our
+experiments, the 8-GPU setting refers to 8 NVIDIA RTX 5880 Ada 48GB GPUs. On
+machines with fewer GPUs, gradient accumulation can be used to approximate the
+same effective batch size. For example, replacing 8 GPUs with 4 GPUs can often
+be approximated by doubling `trainer.accumulate_grad_batches`.
+
+Most provided training commands use `trainer.precision=bf16-mixed`. Geometry,
+depth probability, entropy-probability, and rasterization-sensitive operations
+are kept in float32 internally where needed.
+
+The provided stage-1 training scripts warm-start from DepthSplat Gaussian
+splatting checkpoints. Download the
+corresponding checkpoints before training:
+
+```bash
+mkdir -p pretrained
+
+# RealEstate10K 2-view 256x256 warm start.
+wget https://huggingface.co/haofeixu/depthsplat/resolve/main/depthsplat-gs-base-re10k-256x256-view2-ca7b6795.pth -P pretrained
+
+# DL3DV 2-6 view 256x448 warm start.
+wget https://huggingface.co/haofeixu/depthsplat/resolve/main/depthsplat-gs-base-dl3dv-256x448-randview2-6-02c7b19d.pth -P pretrained
+```
+
+Stage-2 training should be initialized from the corresponding stage-1
+checkpoint. Update the `checkpointing.pretrained_model` paths in
+`scripts/train_*_stage2.sh` to your stage-1 output.
+
+## Camera Conventions
+
+The camera intrinsic matrices are normalized, with the first row divided by the
+image width and the second row divided by the image height.
+
+The camera extrinsic matrices follow the OpenCV camera-to-world convention:
+`+X` right, `+Y` down, and `+Z` pointing into the scene.
+
+## Citation
+
+If you find this project useful, please cite CodecSplat:
+
+```bibtex
+@article{yu2026codecsplat,
+  title   = {CodecSplat: Ultra-Compact Latent Coding for Feed-Forward 3D Gaussian Splatting},
+  author  = {Yu, Pengpeng and Jiang, Runqing and Zhang, Qi and Li, Dingquan and Wang, Jing and Guo, Yulan},
+  journal = {arXiv preprint arXiv:2605.25563},
+  year    = {2026}
+}
+```
+
+## Acknowledgements
+
+This project builds on several excellent open-source projects, including
+[DepthSplat](https://github.com/cvg/depthsplat),
+[ReSplat](https://github.com/cvg/resplat),
+[gsplat](https://github.com/nerfstudio-project/gsplat),
+[Depth Anything V2](https://github.com/DepthAnything/Depth-Anything-V2), and
+[lossy-vae](https://github.com/duanzhiihao/lossy-vae).
+
+We also thank the dataset authors of
+[RealEstate10K](https://google.github.io/realestate10k/) and
+[DL3DV](https://github.com/DL3DV-10K/Dataset).
+We are grateful to the authors and maintainers for making their work available.

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