fuse_to_voxel — 3D `fusion` op

• Data kinds: any → voxel

• Call: import fullseye as fs; fs.ledger.fuse_to_voxel(items, size=64, bounds=None, device='cpu', smooth=0.8) (to call the implementation directly, import fuse3d; fuse3d.fuse_to_voxel(items, size=64, bounds=None, device='cpu', smooth=0.8); from the registry, ops3d.get("fuse_to_voxel"))

• Return value through the ledger: fullseye.ledger.fuse_to_voxel(...) returns **only the declared out type voxel** (the underlying function also returns auxiliary values). When you need what was dropped, use fullseye.ledger.fuse_to_voxel.raw(...) or call fuse3d.fuse_to_voxel directly.

• Underlying return: (voxel, bounds)

• GPU: this op has a GPU path (device="cuda")

Usage

Fuse several structures into one common density voxel volume (TRIZ integration). items=[(data, kind, params_dict), ...].

> The detailed description below is the original text — the summary and the headings are translated.

mesh(topology)+ points(sample)+ depth(観測)等の相補的な構造を 1 表現に。返り値 (voxel, bounds)。

手順: 各 `(data, kind, params) を to_points(data, kind, **params)` で点群にして

縦に連結し、`match3d.points_to_voxel で size^3` の密度 grid に splat する

(各点が落ちる cell を +1、`smooth > 0 なら σ = smooth` voxel の Gaussian で

平滑)。`bounds=None なら連結点群の (min, max)` を格子範囲にする。

引数:

• `items: 空でない list/tuple で、各要素が長さ 3 の (data, kind, params_dict)`

(`params_dict は dict 必須)。to_points の samples` を変えたければ

`params に samples=` を入れる。

• `size`: 1 軸の voxel 数(立方格子固定)。

• `bounds: (lo, hi)` それぞれ長さ 3。複数の雲を同じ格子に載せるとき(比較・

`voxel_iou`)は必ず明示する。

• `device: torch デバイス("cpu" / "cuda")。smooth`: Gaussian σ [voxel]。

返り値: `(voxel, bounds) — voxel は (size, size, size)` float64 の

点数密度(合計 ≈ 総点数、確率ではない)、`bounds は実際に使った (lo, hi)`。

格子 index は `points_to_voxel の floor((p - lo) / span * (size - 1))` で、

軸 0 が点の x 成分に対応する(voxel の軸順 = 点の成分順)。

検証(`ValueError): items` が list/tuple でない・空・要素が 3 組でない・

`params` が dict でない(生データを直接渡す誤用を入口で止める)。

Background guides (the physics and conventions behind this op)

• depth_sensors — 深度センサの知識 — 測距原理・実機の値・欠測の出方

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

Runnable examples (verified samples that actually call this op)

• transforms_repr — py -3.11 examples_3d/transforms_repr.py

Ops the type connects to (they accept `voxel` as input)

voxel_to_mips · voxel_to_mesh · signed_distance_field · to_points · sobel3d · hessian3d · curvature_maps · edt_jfa

Same category (`fusion`)

register_cross

*Provenance: fuse3d.py — 3D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

fuse_to_voxel — 3D fusion op