| --- |
| license: cc-by-nc-sa-4.0 |
| pretty_name: Sensor2Sensor precomputed caches (nuScenes 850 scenes) |
| tags: |
| - lidar |
| - nuscenes |
| - diffusion |
| - dinov3 |
| - sensor-synthesis |
| extra_gated_prompt: >- |
| These are features/latents DERIVED from the nuScenes dataset |
| (CC BY-NC-SA 4.0, non-commercial). By requesting access you agree to the |
| nuScenes Terms of Use (https://www.nuscenes.org/terms-of-use) and to |
| non-commercial use only. No raw nuScenes images or LiDAR points are included, |
| and none are recoverable from these lossy encoder outputs. |
| extra_gated_fields: |
| I agree to the nuScenes non-commercial Terms of Use: checkbox |
| I will use these caches for non-commercial research only: checkbox |
| --- |
| |
| # Sensor2Sensor — precomputed training caches (nuScenes, 850 scenes) |
|
|
| Precomputed **frozen-encoder outputs** for training the DINOv3-conditioned LiDAR |
| diffusion model in **[github.com/skr3178/sensor2sensor](https://github.com/skr3178/sensor2sensor)** |
| **without downloading the 57 GB raw nuScenes dataset**. |
|
|
| Model weights: **[huggingface.co/sangramrout/sensor2sensor](https://huggingface.co/sangramrout/sensor2sensor)**. |
|
|
| ## Contents |
|
|
| Two **matched** caches — 34,149 CAM_FRONT + LIDAR_TOP keyframes (all 850 nuScenes |
| v1.0-trainval scenes), joined by the `sample_token` filename. Each is a tarball of |
| per-token `.npz` files. |
|
|
| ### `cached_latents_v5_850scenes.tar` (3.3 GB) |
| | key | shape / dtype | role | |
| |---|---|---| |
| | `mu` | `[8,8,256]` f32 | **diffusion target** — v5 LiDAR-VAE latent | |
| | `raymap` | `[6,32,56]` f32 | conditioning — ray directions | |
| | `image_latent` | `[4,32,56]` f32 | legacy SD-1.5 image cond (unused by the DINOv3 model) | |
| | `sample_token` | scalar str | nuScenes sample token | |
|
|
| ### `cached_dinov3_v5_850scenes.tar` (7.7 GB) |
| | key | shape / dtype | role | |
| |---|---|---| |
| | `feat` | `[384,14,24]` f16 | **image conditioning** — DINOv3 features (upsampled → 32×56 at train time) | |
| | `sample_token` | scalar str | nuScenes sample token | |
|
|
| **You need both**: `feat` + `raymap` (conditioning) → predict `mu` (target). The DINOv3 |
| cache has no target; the latents cache has no DINOv3 conditioning. |
|
|
| ## Provenance (frozen encoders) |
|
|
| - **Image latent** (`image_latent`): SD-1.5 VAE |
| - **LiDAR latent** (`mu`): v5 LiDAR VAE @ step 3933 (`lidar_vae_best.pt`, on the model repo) |
| - **DINOv3** (`feat`): `vit_small_patch16_dinov3.lvd1689m`, input 224×384 |
|
|
| Built with `s2s_min/train/cache_latents.py` and `s2s_min/train/cache_dinov3.py` in the |
| GitHub repo. Subset: `np.random.default_rng(0).choice(850, 850, replace=False)`. |
|
|
| ## Usage |
|
|
| ```bash |
| hf download sangramrout/sensor2sensor-cache --repo-type dataset --local-dir ./cache |
| tar xf ./cache/cached_latents_v5_850scenes.tar -C s2s_min/out/ |
| tar xf ./cache/cached_dinov3_v5_850scenes.tar -C s2s_min/out/ |
| # then train per the GitHub README |
| ``` |
|
|
| ## License |
|
|
| Derived from **nuScenes** (CC BY-NC-SA 4.0), **non-commercial**, subject to the |
| [nuScenes Terms of Use](https://www.nuscenes.org/terms-of-use). No raw sensor data is |
| included or recoverable from these encodings. |
|
|
