| """Fixed points of the SplatScore scale for every task, on the held-out views. |
| |
| uv run python scripts/baselines.py -> results/baselines.json |
| |
| * empty scene 0 by definition |
| * one_gaussian one Gaussian at the visual-hull center, sized to the hull, in the mean object color |
| * gd64 gradient-descent fit with 64 Gaussians (data/bench/scenes/<scene>/oracle64.npz) |
| * gd300 gradient-descent fit with 300 Gaussians, the same cap and photos as the model: 100 by definition |
| * scrambled_start reassembly tasks only: the scrambled start, submitted unchanged |
| """ |
| import argparse |
| import glob |
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
| import torch |
|
|
| from splatbench.env import load_task |
| from splatbench.optimize import visual_hull_points |
| from splatbench.scene import Scene |
| from splatbench.score import evaluate, splat_score |
|
|
| ap = argparse.ArgumentParser() |
| ap.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") |
| ap.add_argument("--out", default="results/baselines.json") |
| a = ap.parse_args() |
| dev = a.device |
|
|
| out = {} |
| for tp in sorted(glob.glob("data/bench/tasks/*.json")): |
| task = load_task(tp) |
| sd = Path(task.scene_dir) |
| sc = Scene.load(sd) |
| norm = json.loads((sd / "norm.json").read_text()) |
| cams, imgs, masks = sc.torch_views("ref", dev) |
| _, occ = visual_hull_points(cams, masks, 1.0, res=64) |
| lo, hi = occ.min(0), occ.max(0) |
| fg = imgs[masks > 0.5].mean(0) |
| f = lambda x: torch.as_tensor(np.asarray(x), dtype=torch.float32, device=dev) |
| blob = dict(means=f([(lo + hi) / 2]), scales=f([(hi - lo) / 4]), quats=f([[1, 0, 0, 0]]), colors=fg[None], |
| opacities=f([0.95])) |
| res = dict(one_gaussian=round(splat_score(evaluate(blob, sc, "test", dev)["mean"], norm["test"])["score"], 2), |
| gd64=round(splat_score(norm["test"]["oracle64"], norm["test"])["score"], 2), gd300=100.0) |
| if task.kind == "assemble": |
| d = np.load(task.init_npz) |
| gs = {k: f(d[k]) for k in ("means", "scales", "quats", "colors", "opacities")} |
| res["scrambled_start"] = round(splat_score(evaluate(gs, sc, "test", dev)["mean"], norm["test"])["score"], 2) |
| out[task.task_id] = res |
| print(task.task_id, res, flush=True) |
| Path(a.out).parent.mkdir(parents=True, exist_ok=True) |
| Path(a.out).write_text(json.dumps(out, indent=1)) |
|
|
