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README.md

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+---
+license: mit
+tags:
+  - lidar
+  - diffusion
+  - autonomous-driving
+  - sensor-synthesis
+  - nuscenes
+  - dinov3
+library_name: pytorch
+---
+
+# Sensor2Sensor — Camera → LiDAR synthesis checkpoints
+
+Pretrained weights for **cross-modal sensor synthesis** on nuScenes: given surround
+camera images, generate the corresponding LiDAR point cloud via a
+**DINOv3-conditioned latent diffusion** model over a compact **LiDAR range-image VAE**.
+
+- **Code / docs:** https://github.com/skr3178/sensor2sensor
+- **Trained on:** a single RTX 3060 (11.6 GB), nuScenes v1.0-trainval.
+- **Scope:** architecture validation on a small compute budget, not paper-level quality.
+
+## Files
+
+| File | Model | Params | Size |
+|---|---|---|---|
+| `lidar_vae_best.pt` | v5 LiDAR range-image VAE (encoder μ + decoder) | ~2.07 M | 8.3 MB |
+| `lidar_unet_best.pt` | 850-scenes DINOv3-conditioned diffusion U-Net (best held-out CD) | — | 59 MB |
+| `lidar_unet_ema.pt`  | EMA weights of the diffusion U-Net | — | 59 MB |
+
+## Held-out metrics (Chamfer distance, metres, lower = better; cfg=3.5, DDIM-25)
+
+| Component | Metric | Value |
+|---|---|---|
+| LiDAR VAE (v5) | `CD-VAE-only` (decode(μ) vs raw) | **0.791 m** |
+| Diffusion U-Net (850-scenes) | `CD-3D-raw` (N=16 held-out) | **1.994 m** |
+| Diffusion U-Net (850-scenes) | `CD-BEV` (N=16 held-out) | **1.220 m** |
+| End-to-end (VAE + diffusion) | `CD-3D-raw` (4 held-out keyframes) | **3.036 m** |
+
+The 850-scenes checkpoint is selected by **held-out Chamfer distance measured in-loop**,
+not by training MSE (a checkpoint with lower training MSE generalized worse). See the
+repo's `s2s_min/RESULTS.md` §15 for the full rationale.
+
+## Loading
+
+```python
+import torch
+ckpt = torch.load("lidar_vae_best.pt", map_location="cuda")
+# state dict keyed by the training-time best l1_range_ema basin; see the GitHub repo
+# (s2s_min/models/) for the matching module definitions.
+```
+
+## License
+
+MIT (see the GitHub repository). nuScenes data is subject to its own license/terms.