We've compared Scanstead to mesh-capture apps before. This one goes a level deeper: why structure beats resolution when the output is an estimate.
Measurement needs identity, not just geometry
A point cloud tells you where surfaces are with impressive density. What it can't tell you is that this patch of points is a wall, that its extent runs corner to corner, or that the hole in it is a window rather than missing data. Every number you extract from a cloud starts with a human supplying that identity by clicking on things.
A parametric model bakes identity in at capture: wall, door, window, room. Once objects have identity, measurement stops being an activity — the wall has a length the way it has a color. Forty numbers appear because forty objects exist.
Structure enables arithmetic
Real takeoff questions are relational: wall area minus its openings; the shared wall counted once, not twice; per-room totals that sum to house totals; doorways as a count. Each is trivial when walls and openings are objects that know about each other — and each is a research project on a cloud of anonymous points.
This is also why parametric averaging measures better, not just more conveniently: a wall fitted across thousands of samples beats any two hand-picked points on a noisy mesh.
Resolution is a cost you carry
The cloud's million points aren't free — they're megabytes to store, seconds to load, and cleanup work to make presentable. The parametric house is a few hundred surfaces: light enough to text, instant on a client's phone. When you want the visual richness too, photos projected onto the parametric surfaces (High Fidelity mode) add the look without dissolving the structure.
The principle
Capture at the level of abstraction you'll bill at. Estimates are written in walls, rooms, and openings — so measure in walls, rooms, and openings. The tool that answers in your invoice's vocabulary is the one that saves the evening.