Archive - RdesignR

What Should You Check First in Point Cloud Data?

Registration, point density and coverage are three of the first things I examine when assessing a dataset.

5 hrs ago

Does the Final Deliverable Define the Point Cloud?

Point cloud requirements should be determined by what the data needs to support next.

Sep 10

Can Processing Fix Poor Field Acquisition?

Processing can correct many point cloud problems, but it cannot recover important geometry that was never captured.

Sep 8

Can a Point Cloud Look Good and Still Be Wrong?

Visual quality can hide registration and positioning problems that make point cloud geometry unreliable.

Sep 3

Industrial Crane & Rail Alignment Using 3D Laser Scanning

Millimetre-level geometric analysis of a complex crane system between industrial buildings

Sep 1

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Is Higher Point Density Always Better?

More points can provide greater detail, but only when that additional data serves the purpose of the project.

Sep 1

August 2026

What Makes a Point Cloud Actually Useful?

Point cloud quality is not about how impressive the dataset looks. It is about whether the data is fit for the purpose of the project.

Aug 27

Field Notes from Reality Capture Work

What I observe, test and learn through hands-on work.

Aug 18

How I Document Reality Capture Projects

Real projects. Technical decisions. Documented workflows.

Aug 18

July 2026

Standardised Reality Capture Across a Nationwide Tourism Office Network

Terrestrial laser scanning, photographic documentation, point cloud processing and floor plan production across six tourism information offices in…

Jul 30

The Reality Capture Workflow

A practical guide to the technologies, workflows and decisions that connect field acquisition with project-ready spatial data.

Jul 30

3D Laser Scanning for Elevator Shaft Geometry Verification

As-built laser scanning and point cloud analysis to identify construction deviations preventing elevator installation.

Jul 29

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