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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 |
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