| --- |
| pretty_name: PowerSim checkpoints and simulation assets |
| tags: |
| - physics-simulation |
| - powerfoam |
| - material-point-method |
| --- |
| # data/ — released assets (not in git) |
|
|
| | Folder | Contents | Used by | |
| |---|---|---| |
| | `powerfoam_ckpt/<name>/` | `model.pt`, `config.yaml`, `cameras_train.pt`, `cameras_test.pt` | every demo (`--checkpoint-config`) | |
| | `mat_prop_ckpt/bonsai_tree/` | `material_field.pt` (per-primitive E, ν recovered by material estimation, paper Fig. 1c), `triplane_fields.pt` (optimiser state), `selection.pt`, `sim_config.json` | `MATERIAL_FIELD=... run_garden_bonsai_poke.sh` | |
| | `selections/<name>/` | boolean primitive masks (`*_mask.pt`), per-scene material fields and other sim inputs | the run script of that scene | |
|
|
| ## foam_evolve bundle |
| |
| | Checkpoint | Size | Scene | |
| |---|---|---| |
| | `garden_v1_bonsai_foreground_edit` | 2.4 GB | MipNeRF-360 garden with the bonsai composited in place of the vase | |
| | `bread_roll@ec2ced65` | 0.35 GB | bread roll (Blender render data) | |
| | `ficus@48d229db` | 0.22 GB | NeRF-synthetic ficus | |
| | `pillow2sofa_gs_fewer_points` | 1.1 GB | PhysGaussian pillow2sofa, distilled to PowerFoam | |
| | `coke_can_mesh_fewer_points` | 0.97 GB | coke can (mesh render) | |
| | `wolf_gs_filled` | 1.4 GB | PhysGaussian wolf, distilled and interior-filled | |
| | `telephone_sfm@2ed8d7fa` | 1.9 GB | captured telephone (non-pinhole COLMAP camera) | |
| | `mic@84daf179` | 0.27 GB | NeRF-synthetic mic | |
|
|
| `cameras_<split>.pt` (written by `scripts/prep/export_cameras.py`) replaces the training images: |
| image size, camera-to-world poses, the eye/right/up vectors of each view and one shared set of |
| camera-space ray directions, from which each view's ray map is rebuilt exactly as PowerFoam's |
| dataset loader does. |
|
|
| ## material_estimation bundle |
| |
| | Path | Content | |
| |---|---| |
| | `powerfoam_ckpt/carnations_sfm@ae005aa7/` | the carnation checkpoint (2.1 GB) + cameras | |
| | `selections/carnations_sfm@ae005aa7/flower_mask_dilated20.pt` | the flower primitives (195 299 of 1.2 M) | |
| | `selections/carnations_sfm@ae005aa7/interior_fill_cache_flower_mask_dilated20.pt` | interior particle filling of that selection (regenerable with `powersim.material_estimation.filling.precompute_interior_fill`) | |
| | `gt_video/carnation/carnation_poke.mp4` | the captured poke video (training camera 0) | |
| | `mat_prop_ckpt/carnation/` | the field recovered for the paper: `material_field.pt` (simulator format), `triplane_fields.pt` / `velocity_field.pt` (stage-2 / stage-1 state dicts), `selection.pt`, `sim_config.json` | |
|
|
| ## foamedit bundle |
|
|
| | Path | Content | |
| |---|---| |
| | `powerfoam_ckpt/garden_v1/`, `powerfoam_ckpt/bonsai_foreground/` | the MipNeRF-360 garden (1.2 M primitives) and the bonsai captured on its own (300 k); `cameras_*.pt` include the image names the voting needs | |
| | `images/garden_v1/<image>.JPG` | the garden's 185 training photos at the checkpoint's resolution (1297×840), input of the mask generator | |
| | `masks/garden_v1/{vase,frond}/<image>.png` | binary 2D masks of the vase and its frond for all 185 garden views (Grounded-SAM, prompts "a vase with a plant." / "a dried palm frond.") | |
| | `selections/garden_v1/vase_and_frond_mask_paper.pt` | the selection used for the paper's composite | |
| | `selections/bonsai_foreground/tree_mask.pt` | the bonsai's tree without the pot (the part that is simulated), over the bonsai checkpoint's primitives | |
|
|
