Choose a target count or minimum spacing, then download a smaller point cloud.
Try the sample pavilion and enter 10,000 as the target point count. Compare the point count before and after by selecting the original and the result in Your clouds.
Subsampling runs on all source points, not just the preview. Random sampling does not guarantee that small features survive. Minimum spacing uses the source coordinate units; no unit conversion is performed.
Use a target count when you need a specific number of points. Use minimum spacing when you want to remove points that are too close together. Neither method reconstructs surfaces or fills gaps.
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For a two-million-point source, a target of 200,000 keeps roughly one tenth of the points. This is an example, not a recommended setting for every scan. Choose a target below the source count, inspect the result and keep more points if small features become hard to distinguish.
Minimum spacing is measured in the source units. If the coordinates are in meters, a spacing of 0.1 means 10 centimeters. Spatial sampling does not promise a particular output count; the count depends on the distribution of the source points.
Each run creates a separate result. Select the original again before testing another setting, so you compare two reductions of the same source. Repeatedly sampling a reduced result cannot recover points already removed.
| Method | Choose it when | Tradeoff |
|---|---|---|
| Target point count | You have a point-count budget | Random selection can remove small or sparsely sampled features |
| Minimum spacing | You want separation between retained points | The output count depends on spacing and the source distribution |
Not necessarily. File size also depends on the format and retained attributes. A PLY result may still be larger than a compressed LAZ source even with fewer points.
If you only need one region, crop that region first. You can then spend your target point count on that region instead of on parts you will discard.