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 without downloading the 57 GB raw nuScenes dataset.
Model weights: 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
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. No raw sensor data is included or recoverable from these encodings.