signed_distance_field — 3D transform op

Data kinds: voxelsdf

Call: import fullseye as fs; fs.ledger.signed_distance_field(vol, device='cpu', iso=0.5) (to call the implementation directly, import match3d; match3d.signed_distance_field(vol, device='cpu', iso=0.5); from the registry, ops3d.get("signed_distance_field"))

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

Usage

Occupancy/density voxels → a signed distance field SDF (inside < 0, outside > 0). edt_jfa applied to both sides.

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

SDF はマッチングに優れた表現(滑らか・勾配=法線・0 等値面=表面)。inside/outside の

ユークリッド距離差で作る。GPU native。voxel↔SDF↔occupancy を相互変換できる。

定義: `occ = vol > iso として d_out = edt_jfa(occ)`(各 voxel から最寄りの占有 voxel

までの距離、占有内では 0)、`d_in = edt_jfa(~occ)`(最寄りの非占有 voxel まで)、

`sdf = d_out − d_in`。外側は +距離、内側は −距離(voxel 単位、ユークリッド)。

voxel 中心同士の距離なので 0 になる voxel は無く、境界の占有 voxel は −1、隣接する

非占有 voxel は +1(0 等値面は voxel の間)。

`iso` は密度→占有の閾値(既定 0.5。個数密度なら「1 点以上」)。全占有・全空の volume は

seed の無い側の距離が 1e6 に飽和し、全占有では −1e6、全空では +1e6 になる(例外は出ない)。

返り値 `(D,H,W)` float32 numpy。

後段: `sdf_to_occupancy で戻す、voxel_to_mesh(sdf, iso=0.0) で面、sobel3d` で法線場。

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_reprpy -3.11 examples_3d/transforms_repr.py

Ops the type connects to (they accept sdf as input)

sdf_to_occupancy · fuse_to_voxel · integrate · extract_surface_points · query_distance · sdf_union · sdf_intersect · sdf_subtract

Same category (transform)

points_to_voxel · gaussians_to_voxel · mesh_to_voxel · mesh_to_points · depth_to_points · voxel_to_mips · voxel_to_mesh · tsdf_from_depth


*Provenance: match3d.py — 3D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.