"""The mesh-size sweep: four objects resampled to about 1k-1M vertices (Open3D: quadric decimation down, Loop subdivision then decimation up); per mesh the standard routes' time and FCP's (encode separately), one thread. cd ~/code/data/meshes/fcp_3010_hull T="env -u PYTHONPATH NUMBA_NUM_THREADS=1 OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1" $T ~/code/data/meshes/fcp_2910_collision/.venv/bin/python scripts/tradeoffs_sizes.py make # out/sizes/_.ply $T ~/code/data/meshes/fcp_2910_collision/.venv/bin/python scripts/tradeoffs_sizes.py o3d # out/tr_sizes_o3d.csv $T ~/code/fcp_devel/.venv/bin/python scripts/tradeoffs_sizes.py run # out/tr_sizes.csv """ import os import sys import time from pathlib import Path import numpy as np STEP = sys.argv[1] sys.argv = [sys.argv[0], "x"] sys.path.insert(0, str(Path(__file__).parent)) from hull import OBJECTS # noqa: E402 ROOT = Path(__file__).resolve().parent.parent OUT = ROOT / "out" SIZES_DIR = OUT / "sizes" SWEEP = ["mug", "dino", "horse", "knot"] TARGETS = [1000, 3000, 10000, 30000, 100000, 300000, 1000000] # vertices ENC = [(64, 512), (128, 2048), (256, 6144)] # layers, atoms QUERY = [("fast", 16, 64), ("default", 64, 128), ("accurate", 128, 256)] # name, directions M, ring samples def med(f, n=11): f() t = [] for _ in range(n): t0 = time.perf_counter() f() t.append(time.perf_counter() - t0) return 1e3 * float(np.median(t)) def make(): import open3d as o3d import trimesh SIZES_DIR.mkdir(parents=True, exist_ok=True) for name in SWEEP: m = trimesh.load(OBJECTS[name], force="mesh") o = o3d.geometry.TriangleMesh(o3d.utility.Vector3dVector(m.vertices), o3d.utility.Vector3iVector(m.faces)) for n in TARGETS: src = o while len(src.vertices) < n: src = src.subdivide_loop(1) res = src.simplify_quadric_decimation(2 * n) if len(src.vertices) > 1.05 * n else src res.remove_unreferenced_vertices() t = trimesh.Trimesh(np.asarray(res.vertices), np.asarray(res.triangles), process=True) t.export(SIZES_DIR / f"{name}_{n}.ply") print(name, n, len(t.vertices), "watertight", t.is_watertight, flush=True) def o3d_step(): import open3d as o3d import pandas as pd import trimesh rows = [] for f in sorted(SIZES_DIR.glob("*.ply")): m = trimesh.load(f, force="mesh") pts = o3d.utility.Vector3dVector(np.asarray(m.vertices, float)) r = dict(mesh=f.stem, verts=len(m.vertices), load=os.getloadavg()[0], o3d_aabb_ms=med(lambda: o3d.geometry.AxisAlignedBoundingBox.create_from_points(pts), 51), o3d_pca_ms=med(lambda: o3d.geometry.OrientedBoundingBox.create_from_points(pts)), o3d_pca_vol=o3d.geometry.OrientedBoundingBox.create_from_points(pts).volume()) rows.append(r) print(r, flush=True) pd.DataFrame(rows).to_csv(OUT / "tr_sizes_o3d.csv", index=False) def run(): import pandas as pd import trimesh from scipy.spatial import ConvexHull import fcp from bounds import min_sphere, outside, _RNG from bounds_fast import fast_bounds3, hull_fast from volume_atoms import volume_atoms_fast from volume_sota import vol_nb assert os.environ.get("NUMBA_NUM_THREADS") == "1" rows = [] for f in sorted(SIZES_DIR.glob("*.ply"), key=lambda p: (p.stem.rsplit("_", 1)[0], int(p.stem.rsplit("_", 1)[1]))): name, n = f.stem.rsplit("_", 1) m = trimesh.load(f, force="mesh") V = np.ascontiguousarray(m.vertices, np.float64) F = np.ascontiguousarray(m.faces, np.int64) diag = float(np.linalg.norm(V.max(0) - V.min(0))) H = ConvexHull(V) true = trimesh.Trimesh(V, H.simplices) true.fix_normals() S, _ = trimesh.sample.sample_surface(true, 5000, seed=0) def exact_sphere(): Hv = V[ConvexHull(V).vertices] return min_sphere(Hv[_RNG.permutation(len(Hv))]) vol = vol_nb(V, F) sr = exact_sphere()[1] tob = float(np.prod(trimesh.bounds.oriented_bounds(m)[1])) base = dict(obj=name, target=int(n), verts=len(V), faces=len(F), load=os.getloadavg()[0], qhull_ms=med(lambda: ConvexHull(V)), tetra_ms=med(lambda: vol_nb(V, F), 51), aabb_np_ms=med(lambda: (V.min(0), V.max(0)), 51), sphere_exact_ms=med(exact_sphere), trimesh_obb_ms=med(lambda: trimesh.bounds.oriented_bounds(m), 3)) for L, A in ENC: t0 = time.perf_counter() d = fcp.encode(m, atoms=A, layers=L, device="cpu") enc = 1e3 * (time.perf_counter() - t0) enc = min(enc, med(lambda: fcp.encode(m, atoms=A, layers=L, device="cpu"), 2)) r = dict(base, layers=L, atoms=A, encode_ms=enc, volume_ms=med(lambda: volume_atoms_fast(d), 51), volume_ratio=volume_atoms_fast(d) / vol) for q, M, nt in QUERY: Vh, Th = hull_fast(d, M, nt) h = trimesh.Trimesh(Vh, Th, process=False) _, dist, _ = trimesh.proximity.closest_point(h, S) o = fast_bounds3(d, nt, M) r.update({f"{q}_hull_ms": med(lambda: hull_fast(d, M, nt)), f"{q}_hull_ratio": h.volume / H.volume, f"{q}_hull_gap_max_pct": 100 * dist.max() / diag, f"{q}_obb_ms": med(lambda: fast_bounds3(d, nt, M, which=("obb",))), f"{q}_obb_ratio": float(np.prod(2 * o["obb"][2]) / tob), f"{q}_sphere_ms": med(lambda: fast_bounds3(d, nt, M, which=("sphere",))), f"{q}_sphere_ratio": float(o["sphere"][1] / sr), f"{q}_out_pct": 100 * max(outside(V, aabb=o["aabb"]), outside(V, obb=o["obb"]), outside(V, sphere=o["sphere"])) / diag}) rows.append(r) print({k: (round(v, 3) if isinstance(v, float) else v) for k, v in r.items() if not k.startswith(("fast_", "accurate_"))}, flush=True) pd.DataFrame(rows).to_csv(OUT / "tr_sizes.csv", index=False) if __name__ == "__main__": {"make": make, "o3d": o3d_step, "run": run}[STEP]()