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README.md

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+---
+license: gpl-3.0
+pipeline_tag: image-to-3d
+tags:
+- novel-view-synthesis
+- neural-rendering
+- nerf
+- 3d
+datasets:
+- RealEstate10K
+- DL3DV-10K
+---
+
+# CLiFT: Compressive Light-Field Tokens for Compute-Efficient and Adaptive Neural Rendering
+
+Pretrained checkpoints for **CLiFT** ([arXiv](https://arxiv.org/abs/2507.08776), [project page](https://clift-nvs.github.io/), [code](https://github.com/eric-zqwang/CLiFT)).
+
+CLiFT represents a scene as compressed light-field tokens (CLiFTs): an encoder tokenizes the input views, latent K-means selects centroid tokens, and a neural condenser aggregates each cluster into its centroid. A single trained model supports compute-adaptive rendering — fewer tokens for lower storage/compute, more tokens for higher quality.
+
+## Checkpoints
+
+| File | Description | Place at (in the [code repo](https://github.com/eric-zqwang/CLiFT)) |
+|---|---|---|
+| `re10k/first_stage.ckpt` | RealEstate10K encoder-decoder (LVSM-style, random token-drop) | `output/re10k_first_stage/training/last.ckpt` |
+| `re10k/second_stage.ckpt` | RealEstate10K condenser (full CLiFT model) | `output/re10k_second_stage/training/last.ckpt` |
+| `dl3dv/first_stage.ckpt` | DL3DV encoder-decoder, fine-tuned from the RE10K model | `output/dl3dv_first_stage/training/last.ckpt` |
+| `dl3dv/second_stage.ckpt` | DL3DV condenser (full CLiFT model) | `output/dl3dv_second_stage/training/last.ckpt` |
+
+The second-stage checkpoints are the full CLiFT models used for the paper's main results. The first-stage checkpoints are used by the random / K-means selection baselines, for K-means annotation, and as the initialization for condenser training.
+
+## Usage
+
+```bash
+git clone https://github.com/eric-zqwang/CLiFT.git
+cd CLiFT
+# download the checkpoints to the paths above, prepare data (see docs/), then e.g.
+bash script/eval/eval_clift.sh       # RE10K
+bash script/eval/eval_dl3dv.sh 6     # DL3DV, 6 context views
+```
+
+See the [test guide](https://github.com/eric-zqwang/CLiFT/blob/main/docs/test.md) for evaluation and the [training guide](https://github.com/eric-zqwang/CLiFT/blob/main/docs/training.md) for the two-stage training pipeline.
+
+## Citation
+
+```bibtex
+@article{Wang2025CLiFT,
+  author    = {Wang, Zhengqing and Wu, Yuefan and Chen, Jiacheng and Zhang, Fuyang and Furukawa, Yasutaka},
+  title     = {CLiFT: Compressive Light-Field Tokens for Compute Efficient and Adaptive Neural Rendering},
+  journal   = {arXiv preprint arXiv:2507.08776},
+  year      = {2025},
+}
+```