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Register & compare

Align two datasets and measure how well they match. Phytograph supports three flavors of ICP plus simple multi-cloud stitching.

Stitch

The simplest case: you have several point clouds of the same plant (e.g., from different scan positions) that are already roughly aligned in world coordinates, and you want a single combined cloud.

  1. Open Stitch Clouds from the Pre-processing toolbar group (merge icon) or Tools → Pre-processing → Stitch Clouds….
  2. In the dialog, check the two or more clouds to merge. (If you had clouds selected in the scene, they're pre-checked — you can change the choice here.)
  3. Optionally tick Keep original clouds to merge non-destructively — see below.
  4. Click Stitch. The clouds are combined in the backend (a brief progress indicator shows while the merged cloud's octree is built), and a single new cloud replaces the originals in the scene.

Stitching is reversible: a single Undo removes the merged cloud and restores the original clouds exactly as they were, so you can re-stitch with a different subset.

Keep original clouds

By default the source clouds are removed from the scene when they merge. Tick Keep original clouds and they stay instead — hidden, so the viewport looks the same, but still listed and still fully usable. Click the eye icon next to one to bring it back.

This is useful when you want the merged cloud for a whole-plot view while keeping the individual scans for per-scan analyses — the retained clouds keep their scanner origins, so Backfill Misses, Helios triangulation, and Leaf Area Density still run on them even though they're unavailable on the merge itself.

Undo with retained originals

With Keep original clouds ticked, Undo removes the merged cloud but leaves the originals hidden — showing and hiding clouds isn't part of the undo history. Click the eye icons to bring them back.

Stitch ≠ register

Stitch concatenates the clouds' points into one cloud. Clouds imported with different global shifts are lined up in true world coordinates automatically, so a stitch never mis-places a shifted cloud. What it does not do is register — it won't correct clouds that are genuinely mis-aligned in the world. For that, use Cloud-to-cloud ICP (below) on each pair first, then stitch.

Stitching discards scanner origins

A stitched cloud has no single scanner origin, so scan parameters (origin, trajectory) from the source clouds are dropped on the merge. That means origin-dependent analyses are unavailable on the merged cloud:

  • Backfill Misses — recovered sky/miss points can't be placed for the viewer overlay (they'd still be computed for LAD directions, but LAD itself is unavailable — see below).
  • Helios triangulation (the Helios method, which projects to per-pulse spherical angles from the scanner origin).
  • Leaf Area Density (needs the beam origin for the Beer's-law inversion).

If any cloud you're stitching carries an origin, the Stitch dialog shows a warning and the button reads Stitch anyway so the loss is a deliberate choice. You have three ways around it: tick Keep original clouds so the sources (and their origins) survive the merge, run these analyses on the individual scans before stitching, or — if the clouds are mis-aligned — register them with Cloud-to-cloud ICP first (that preserves each scan's own origin) rather than stitching.

The underlying points, colors, intensity, and scalar attributes are all preserved (attributes present on only some inputs are carried through and filled with zeros for the clouds that lacked them).

Cloud-to-cloud ICP

Align one cloud to another by iteratively minimizing point-to-point distance.

  1. Open Align Clouds (ICP) from the Pre-processing toolbar group (globe icon) or Tools → Registration → Align Clouds (ICP)….
  2. In the dialog, pick the target (stays fixed) and the source (moves onto the target). Either can be any cloud — a large streamed cloud can be the source too; its transform is applied on the backend and its octree rebuilt.
  3. Click Align.

ICP runs and reports:

  • RMSE — root-mean-square distance after alignment
  • Overlap % — the fitness score, i.e. the share of source points that found a correspondence
  • A transformation matrix applied to the source cloud

ICP is not undoable

The source cloud is updated in place and its backend session rewritten, so ⌘/Ctrl+Z will not revert an alignment (it pops whatever edit preceded it). Duplicate the scan first if you want to keep the pre-alignment version.

Read the RMSE against the cloud's own extent, not the overlap figure — fitness can read near-100% on a badly wrong alignment.

Unlike stitching, ICP preserves the source's scan parameters: the scanner origin (and, for a moving-platform scan, the whole trajectory) is moved by the same rigid transform as the points, so it stays consistent with the aligned cloud. Origin-dependent analyses (LAD, triangulation) therefore keep working on a registered source scan.

Mesh-to-mesh ICP

Same idea as cloud-to-cloud but on surfaces.

  1. Run Align Mesh to Mesh (ICP)… from Tools → Registration or the command palette (⌘/Ctrl+K).
  2. In the dialog, pick the target (stays fixed) and the source (moves onto the target). If you had meshes selected in the scene, they're pre-picked — you can change the choice here.
  3. Click Align. The source mesh is transformed to best fit the target; the toast reports the fit. As with the other ICP tools, this is not undoable.

Mesh-to-mesh is typically more accurate than cloud-to-cloud because points are sampled uniformly off both surfaces, giving a denser and more even correspondence set than raw scan points.

Cloud-to-mesh distance

Measure how well a mesh fits a point cloud — e.g., comparing a real scan against a procedural model or any cloud-versus-mesh ground truth — without moving anything.

  1. Run Cloud-to-Mesh Distance… from Tools → Registration or the command palette.
  2. In the dialog, pick the point cloud and the mesh. (Pre-picked from the scene selection when available.)
  3. Click Compute Distance.

The Alignment panel opens with point-to-mesh distance statistics:

  • Mean / Median / RMSE and standard deviation
  • Min / Max distance and the 90th / 95th / 99th percentiles
  • Coverage — the share of cloud points within three distance bands. The panel labels these < 1mm, < 5mm, and < 10mm, but they are relative, not absolute: the thresholds are 0.1%, 0.5%, and 1% of the cloud's bounding-box diagonal. On a 10 m cloud, "< 1mm" means within 10 mm
  • The point count the statistics were computed from

Cloud-to-mesh ICP (snap to fit)

To actually move a mesh onto a cloud:

  1. Run Align Mesh to Cloud (ICP)… from Tools → Registration or the command palette.
  2. In the dialog, pick the point cloud (stays fixed) and the mesh (moves onto it). Click Snap to Fit (ICP).

The mesh is transformed to best fit the cloud, and the toast reports the RMSE (the average residual distance after alignment) alongside the overlap percentage. Like the other ICP tools, this is not undoable.

Read the RMSE, not the overlap percentage

Overlap (ICP's "fitness") is the share of points that found a match — it reaches 100% whenever the search radius is generous relative to the object, including for a visibly wrong alignment. RMSE is the honest number: a good registration lands well under 1% of the cloud's size. When the residual is large relative to the scene, the toast turns into a warning telling you to review the result before keeping it, rather than reporting success.

Distance then snap

The Alignment panel from a Cloud-to-Mesh Distance run also has a Snap to Fit (ICP) button that registers the same cloud + mesh you just measured — so you can check the fit, then snap, in one place.

Progress and cancelling

Every registration and the cloud-to-mesh distance run shows a progress pill at the top of the viewport while it works — the ICP tools advance it per iteration batch with the current RMSE. Click the ✕ on the pill to cancel; the alignment stops (within one iteration batch) and nothing is moved, exactly as if it hadn't been run.

When ICP fails

ICP finds a local minimum, so it needs the inputs to be roughly pre-aligned. If RMSE comes back huge, or the result looks visibly wrong:

  1. Pre-align manually with Transform — translate and rotate to within ~10 cm and a few degrees before running ICP.
  2. Crop away non-overlapping regions so the correspondence search isn't dominated by geometry the other input doesn't contain.

The ICP dialogs are deliberately minimal — pick a source, pick a target, run. There are no voxel-size or iteration-count settings to tune, so improving a bad result means improving the inputs.

For very different inputs (e.g., a sparse cloud and a dense mesh), expect higher RMSE than for similar-density inputs.