What is point cloud registration?
Registration estimates the transformation between scans. It is a preparation step for combining surveys and comparing dates, not a change detector.
Approaches and starting model
Traditional geometric matching and ICP provide useful baselines. Learned matching methods such as GeoTransformer use correspondences to estimate a rigid alignment.
Inputs and outputs
Two overlapping XYZ clouds are processed with matching sampling and units. Output is a rigid rotation and translation, together with correspondence information used by the implementation.
Where it helps in GIS and digital twins
Align repeat survey fragments before comparison or visualization. Keep the original coordinate transforms and control points; convert the final alignment into the digital twin's reference frame.
How to start
Use paired clouds and known relative poses with the selected experiment's data preparation. Fine-tuning needs representative overlap and sensor conditions. Reserve independent routes for evaluation.
How to evaluate results
Check residuals on stable surfaces and independent control, not only fitted correspondences. Compare against a traditional registration baseline. Distinguish alignment error from actual scene change.
Limitations
Rigid registration cannot explain real deformation. Repeated structures, moving objects and little overlap can produce plausible but wrong alignment. The documented environment contains older compiled dependencies.
Official starting-model documentation
The category artwork illustrates its starting model; it is not an evaluated prediction.