python.workflows.clfSPTPreprocess Namespace Reference

Preprocess point clouds into the SPT partition cache (NAG format) More...

Classes
class	clfSPTPreprocess

Variables
	message
	category
	UserWarning
	module
	SPT_ROOT = os.path.join(_DISTRO_ROOT, 'addons', 'superpoint_transformer', 'superpoint_transformer')
	SPT_CONFIG_DIR = os.path.join(SPT_ROOT, 'configs')
	script

Detailed Description

Preprocess point clouds into the SPT partition cache (NAG format)

clfSPTPreprocress is the preprocessing step for the training workflow (clfSPTSetup -> clfSPTPreprocess -> clfSPTTrain) and the inference workflow (clfSPTPreprocess -> clfSPTInfer). It converts raw point clouds into hierachical graph structues (NAGs) on which SPT operates on. The module runs in two modes:

In training mode, it reads all tiles from the project's split folders, applies the pretransforms from the <project>.cfg file and saves them in the preprocessed folder. In inference mode, it processes individual tiles using the frozen config from the training run. Large tiles can be cut into smaller sub-tiles which will be saved alongside the input files. The core operation in both cases is the same: a raw point cloud is voxelised, partitioned into geometrically homohogenous superpoints and stored as NAG. The difference lies in where the partitioning parameters come from. At the end of every run, partition quality metrics are reported.

See also

Script documentation