Point Cloud Processing API

Module: mls.point_cloud. This page documents 24 public functions with exact tool IDs, complete parameters, Parameters fields, and examples.

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Functions

pointcloud_extract_by_elevation — Extract point cloud data by elevation range. Supports single file or batch processing with input_paths array (output_path will be used as directory)
pointcloud_extract_by_class — Extract point cloud by class
pointcloud_extract_by_intensity — Extract point cloud data by intensity range. Supports single file or batch processing with input_paths array (output_path will be used as directory)
pointcloud_extract_by_return — Extract point cloud data by return number. Supports single file or batch processing with input_paths array (output_path will be used as directory)
ortho_photo — Ortho Photo Tool
pointcloud_transform_gps_time — Transform the timestamp of the point cloud
pointcloud_compute_normals — Calculate the normal of the point cloud
pointcloud_normalize_by_ground_points — Normalize the point cloud by ground point
pointcloud_denormalization — Denormalize the point cloud
pointcloud_merge — Merge multiple point clouds into one
pointcloud_subsampling — Subsample the point cloud
pointcloud_noise_filter — Perform noise filter on the point cloud
pointcloud_remove_outliers — Remove outliers from the point cloud
pointcloud_smooth — Smooth point cloud method to reduce noise and improve point cloud quality (output_path will be used as directory)
pointcloud_brightness_contrast — Adjust point cloud brightness and contrast
pointcloud_cut_long_range — Cut or classify long range points based on trajectory distance
pointcloud_cutoverlap — Classify or delete overlap points based on trajectory
pointcloud_assign_group — Assign groups to point cloud points
pointcloud_moving_objects — Detect moving objects from point cloud
pointcloud_feature_computor — Compute geometric features of point cloud
pointcloud_points_deduplicator — Remove duplicate points from point cloud
pointcloud_adaptive_downsample — Adaptive downsampling of point cloud
pointcloud_density_analysis — Density quality analysis of point cloud
convertprojectedsurface — Convert point cloud to projected surface for compensation

`pointcloud_extract_by_elevation`

Extract point cloud data by elevation range. Supports single file or batch processing with input_paths array (output_path will be used as directory)

Exact tool ID:pointcloud_extract_by_elevation
Recommended call:mls.point_cloud.pointcloud_extract_by_elevation(...)
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	No	`''`	Output folder. If the user does not specify, set this parameter to ""
`Parameters`	`object`	Yes	`—`	Elevation extraction parameters

`Parameters` fields

Field	Type	Required	Default	Description
`minElevation`	`number`	No	`100.0`	Minimum elevation value
`maxElevation`	`number`	No	`200.0`	Maximum elevation value

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_extract_by_elevation_parameters()
params.minElevation = 100.0

result = mls.point_cloud.pointcloud_extract_by_elevation(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
    output_path='',
)
print(result.ok, result.output, result.message)

`pointcloud_extract_by_class`

Extract point cloud by class

Exact tool ID:pointcloud_extract_by_class
Recommended call:mls.point_cloud.pointcloud_extract_by_class(...)
Required parameters:input_paths, class

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	No	`''`	Output folder. If the user does not specify, set this parameter to ""
`class`	`array`	Yes	`—`	Class IDs to extract (e.g., ["1","2"])

Example

from gvscript import mls

kwargs = {'class': ['example']}

result = mls.point_cloud.pointcloud_extract_by_class(
    input_paths=['D:/data/input.LiData'],
    output_path='',
    **kwargs,
)
print(result.ok, result.output, result.message)

`pointcloud_extract_by_intensity`

Extract point cloud data by intensity range. Supports single file or batch processing with input_paths array (output_path will be used as directory)

Exact tool ID:pointcloud_extract_by_intensity
Recommended call:mls.point_cloud.pointcloud_extract_by_intensity(...)
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	No	`''`	Output folder. If the user does not specify, set this parameter to ""
`Parameters`	`object`	Yes	`—`	intensity extraction parameters

`Parameters` fields

Field	Type	Required	Default	Description
`minIntensity`	`number`	No	`0`	Minimum intensity value
`maxIntensity`	`number`	No	`20000`	Maximum intensity value

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_extract_by_intensity_parameters()
params.minIntensity = 0

result = mls.point_cloud.pointcloud_extract_by_intensity(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
    output_path='',
)
print(result.ok, result.output, result.message)

`pointcloud_extract_by_return`

Extract point cloud data by return number. Supports single file or batch processing with input_paths array (output_path will be used as directory)

Exact tool ID:pointcloud_extract_by_return
Recommended call:mls.point_cloud.pointcloud_extract_by_return(...)
Required parameters:input_paths

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`return_numbers`	`array`	No	`—`	Return numbers to extract. Valid values: 'first', 'last', '1'-'7'

Example

from gvscript import mls

result = mls.point_cloud.pointcloud_extract_by_return(
    input_paths=['D:/data/input.LiData'],
    return_numbers=['first', 'last', '1', '2'],
)
print(result.ok, result.output, result.message)

`ortho_photo`

Ortho Photo Tool

Exact tool ID:Ortho_Photo
Recommended call:mls.point_cloud.ortho_photo(...)
Alias:Ortho_Photo
Required parameters:output_path

Parameters

Parameter	Type	Required	Default	Description
`input_path`	`string`	No	`—`	Input point cloud file path (.LiData)
`output_path`	`string`	Yes	`—`	Output file path
`m_resolution`	`number`	No	`0.01`	Image resolution
`m_mode`	`number`	No	`2`	Export orthophotos by elevation, intensity, density, or RGB
`m_coloraize`	`number`	No	`0`	Colorized?
`m_use_pcos_box`	`number`	No	`1`	Whether to use the point cloud bounding box
`result_name`	`string`	No	`—`	Result name for layer

Example

from gvscript import mls

result = mls.point_cloud.ortho_photo(
    output_path='D:/data/output',
    input_path='D:/data/input.LiData',
    m_resolution=0.01,
    m_mode=2,
    m_coloraize=0,
    m_use_pcos_box=1,
    result_name='example_name',
)
print(result.ok, result.output, result.message)

`pointcloud_transform_gps_time`

Transform the timestamp of the point cloud

Exact tool ID:PointCloud_Transform_GPS_Time
Recommended call:mls.point_cloud.pointcloud_transform_gps_time(...)
Alias:PointCloud_Transform_GPS_Time
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	No	`''`	Output folder. If the user does not specify, set this parameter to ""
`Parameters`	`object`	Yes	`—`	GPS time transformation parameters

`Parameters` fields

Field	Type	Required	Default	Description
`method`	`integer`	No	`0`	1: source and target system; 2: Multiply and add constant
`inputTimeMode`	`integer`	No	`0`	0: UTC seconds of week; 1: UTC seconds of day; 2: GPS seconds of week; 3: GPS seconds of day; 4: GPS standard time; 5: GPS time; 6: unix
`outputTimeMode`	`integer`	No	`0`	0: UTC seconds of week; 1: UTC seconds of day; 2: GPS seconds of week; 3: GPS seconds of day; 4: GPS standard time; 5: GPS time; 6: unix
`date`	`string`	No	`—`	Data collection time in yyyy-mm-dd format
`factor`	`number`	No	`1`	Multiplication factor
`add`	`number`	No	`0`	Addition constant
`isTraj`	`string`	No	`'0'`	Is trajectory
`isImagelist`	`string`	No	`'0'`	Is image list

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_transform_gps_time_parameters()
params.method = 0

result = mls.point_cloud.pointcloud_transform_gps_time(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
    output_path='',
)
print(result.ok, result.output, result.message)

`pointcloud_compute_normals`

Calculate the normal of the point cloud

Exact tool ID:PointCloud_Compute_Normals
Recommended call:mls.point_cloud.pointcloud_compute_normals(...)
Alias:PointCloud_Compute_Normals
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	No	`''`	Output folder. If the user does not specify, set this parameter to ""
`Parameters`	`object`	Yes	`—`	Normals computation parameters

`Parameters` fields

Field	Type	Required	Default	Description
`SearchRadius`	`number`	No	`0.1`	Search Radius
`NormalConsistency`	`string`	No	`'1'`	Normal consistency
`trajPath`	`string`	No	`—`	Input trajectory file path

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_compute_normals_parameters()
params.SearchRadius = 0.1

result = mls.point_cloud.pointcloud_compute_normals(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
    output_path='',
)
print(result.ok, result.output, result.message)

`pointcloud_normalize_by_ground_points`

Normalize the point cloud by ground point

Exact tool ID:PointCloud_Normalize_by_Ground_Points
Recommended call:mls.point_cloud.pointcloud_normalize_by_ground_points(...)
Alias:PointCloud_Normalize_by_Ground_Points
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	No	`''`	Output folder. If the user does not specify, set this parameter to ""
`Parameters`	`object`	Yes	`—`	Normalization parameters

`Parameters` fields

Field	Type	Required	Default	Description
`searchType`	`integer`	No	`0`	0: 2D; 1: 3D
`keepZValueToAtt`	`string`	No	`'1'`	Keep Z value to attribute
`resolution`	`number`	No	`0.1`	Resolution

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_normalize_by_ground_points_parameters()
params.searchType = 0

result = mls.point_cloud.pointcloud_normalize_by_ground_points(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
    output_path='',
)
print(result.ok, result.output, result.message)

`pointcloud_denormalization`

Denormalize the point cloud

Exact tool ID:PointCloud_Denormalization
Recommended call:mls.point_cloud.pointcloud_denormalization(...)
Alias:PointCloud_Denormalization
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	No	`''`	Output folder. If the user does not specify, set this parameter to ""
`Parameters`	`object`	Yes	`—`	Denormalization parameters

`Parameters` fields

Field	Type	Required	Default	Description
`Denormalization_isUseCreated`	`string`	No	`'0'`	Use created DEM
`Denormalization_DEMFile`	`string`	No	`''`	DEM file path

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_denormalization_parameters()
params.Denormalization_isUseCreated = '0'

result = mls.point_cloud.pointcloud_denormalization(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
    output_path='',
)
print(result.ok, result.output, result.message)

`pointcloud_merge`

Merge multiple point clouds into one

Exact tool ID:PointCloud_Merge
Recommended call:mls.point_cloud.pointcloud_merge(...)
Alias:PointCloud_Merge
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	No	`''`	Output folder. If the user does not specify, set this parameter to ""
`Parameters`	`object`	Yes	`—`	Merge parameters

`Parameters` fields

Field	Type	Required	Default	Description
`bIgnoreDifferentAtt`	`integer`	No	`1`	1: Ignore different attributes; 0: Do not ignore

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_merge_parameters()
params.bIgnoreDifferentAtt = 1

result = mls.point_cloud.pointcloud_merge(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
    output_path='',
)
print(result.ok, result.output, result.message)

`pointcloud_subsampling`

Subsample the point cloud

Exact tool ID:PointCloud_Subsampling
Recommended call:mls.point_cloud.pointcloud_subsampling(...)
Alias:PointCloud_Subsampling
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	No	`''`	Output folder. If the user does not specify, set this parameter to ""
`Parameters`	`object`	Yes	`—`	Subsampling parameters

`Parameters` fields

Field	Type	Required	Default	Description
`samplType`	`integer`	Yes	`0`	0: Voxel; 1: Minimum points spacing; 2: sampling rate
`Rate`	`number`	No	`99.99`	Sampling rate (0-100%). Only used when samplType=2
`VoxelSize`	`number`	No	`0.5`	Voxel size. Only used when samplType=0
`PointsSpace`	`integer`	No	`1`	Minimum points spacing. Only used when samplType=1

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_subsampling_parameters()
params.samplType = 0

result = mls.point_cloud.pointcloud_subsampling(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
    output_path='',
)
print(result.ok, result.output, result.message)

`pointcloud_noise_filter`

Perform noise filter on the point cloud

Exact tool ID:PointCloud_Noise_Filter
Recommended call:mls.point_cloud.pointcloud_noise_filter(...)
Alias:PointCloud_Noise_Filter
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	No	`''`	Output folder. If the user does not specify, set this parameter to ""
`Parameters`	`object`	Yes	`—`	Noise filter parameters

`Parameters` fields

Field	Type	Required	Default	Description
`NeighborsType`	`integer`	No	`1`	1: custom radius; 2: recommend radius
`KnnNeighbor`	`string`	No	`'6'`	KNN neighbor count
`Radius`	`number`	No	`0.5`	Search radius. Required when NeighborsType=1
`MaxErrorType`	`string`	No	`'1'`	Max error type
`Relatiev_MaxError`	`string`	No	`'1'`	Relative max error
`Absolute_MaxError`	`string`	No	`'1'`	Absolute max error
`ifRemoveIsolatePoints`	`string`	No	`'0'`	Remove isolated points
`bufferSize`	`string`	No	`'2'`	Buffer size

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_noise_filter_parameters()
params.NeighborsType = 1

result = mls.point_cloud.pointcloud_noise_filter(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
    output_path='',
)
print(result.ok, result.output, result.message)

`pointcloud_remove_outliers`

Remove outliers from the point cloud

Exact tool ID:PointCloud_Remove_Outliers
Recommended call:mls.point_cloud.pointcloud_remove_outliers(...)
Alias:PointCloud_Remove_Outliers
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	No	`''`	Output folder. If the user does not specify, set this parameter to ""
`Parameters`	`object`	Yes	`—`	Outlier removal parameters

`Parameters` fields

Field	Type	Required	Default	Description
`mulStdDeviation`	`integer`	No	`5`	Multiples of standard deviation
`neighborPoints`	`integer`	No	`10`	Number of neighboring points

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_remove_outliers_parameters()
params.mulStdDeviation = 5

result = mls.point_cloud.pointcloud_remove_outliers(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
    output_path='',
)
print(result.ok, result.output, result.message)

`pointcloud_smooth`

Smooth point cloud method to reduce noise and improve point cloud quality (output_path will be used as directory)

Exact tool ID:pointcloud_smooth
Recommended call:mls.point_cloud.pointcloud_smooth(...)
Required parameters:input_paths, output_path, radius

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	Yes	`''`	Output folder. If the user does not specify, set this parameter to ""
`radius`	`number`	Yes	`0.2`	Smoothing search radius
`pos_file`	`string`	No	`—`	Trajectory file path (*.pos) for motion correction
`result_name`	`string`	No	`—`	Result name for layer

Example

from gvscript import mls

result = mls.point_cloud.pointcloud_smooth(
    input_paths=['D:/data/input.LiData'],
    output_path='',
    radius=0.2,
    pos_file='example',
    result_name='example_name',
)
print(result.ok, result.output, result.message)

`pointcloud_brightness_contrast`

Adjust point cloud brightness and contrast

Exact tool ID:pointcloud_brightness_contrast
Recommended call:mls.point_cloud.pointcloud_brightness_contrast(...)
Required parameters:input_paths, output_path, brightness, contrast

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	Yes	`—`	Output file path
`brightness`	`integer`	Yes	`0`	Brightness adjustment value
`contrast`	`integer`	Yes	`0`	Contrast adjustment value
`base_brightness`	`integer`	No	`0`	Base brightness value
`result_name`	`string`	No	`—`	Result name for layer

Example

from gvscript import mls

result = mls.point_cloud.pointcloud_brightness_contrast(
    input_paths=['D:/data/input.LiData'],
    output_path='D:/data/output',
    brightness=0,
    contrast=0,
    base_brightness=0,
    result_name='example_name',
)
print(result.ok, result.output, result.message)

`pointcloud_cut_long_range`

Cut or classify long range points based on trajectory distance

Exact tool ID:pointcloud_cut_long_range
Recommended call:mls.point_cloud.pointcloud_cut_long_range(...)
Required parameters:input_paths, action_type, mode, traj_path

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`action_type`	`string`	Yes	`'Classify'`	Action type
`mode`	`string`	Yes	`'Absolute'`	Mode
`search_radius`	`number`	No	`50.0`	Search radius
`range_difference`	`number`	No	`10.0`	Range difference threshold
`factor`	`number`	No	`1.0`	Factor value
`traj_path`	`string`	Yes	`—`	Trajectory file path (*.pos)
`from_class`	`any`	No	`'1'`	Source class filter (comma-separated class IDs, e.g., '1,2,3')
`to_class`	`integer`	No	`—`	Target class ID for classification

Example

from gvscript import mls

result = mls.point_cloud.pointcloud_cut_long_range(
    input_paths=['D:/data/input.LiData'],
    action_type='Classify',
    mode='Absolute',
    search_radius=50.0,
    range_difference=10.0,
    factor=1.0,
    traj_path='D:/data/input.LiData',
    from_class='1',
    to_class=1,
)
print(result.ok, result.output, result.message)

`pointcloud_cutoverlap`

Classify or delete overlap points based on trajectory

Exact tool ID:pointcloud_cutoverlap
Recommended call:mls.point_cloud.pointcloud_cutoverlap(...)
Required parameters:input_paths, action_type, method_type

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`action_type`	`string`	Yes	`'Classify'`	Action type
`method_type`	`string`	Yes	`'ByOffset'`	Method type
`offset`	`number`	No	`25.0`	Offset threshold
`scan_angle`	`number`	No	`10.0`	Scan angle threshold
`edge`	`number`	No	`1.0`	Edge threshold
`density`	`number`	No	`10.0`	Density threshold
`traj_path`	`string`	No	`—`	Trajectory file path (*.pos)
`output_path`	`string`	No	`—`	Output file path
`from_class`	`string`	No	`'1'`	Source class filter (comma-separated class IDs, e.g., '1,2,3')
`to_class`	`integer`	No	`—`	Target class ID for classification

Example

from gvscript import mls

result = mls.point_cloud.pointcloud_cutoverlap(
    input_paths=['D:/data/input.LiData'],
    output_path='D:/data/output',
    action_type='Classify',
    method_type='ByOffset',
    offset=25.0,
    scan_angle=10.0,
    edge=1.0,
    density=10.0,
    traj_path='D:/data/input.LiData',
    from_class='1',
    to_class=1,
)
print(result.ok, result.output, result.message)

`pointcloud_assign_group`

Assign groups to point cloud points

Exact tool ID:PointCloud_Assign_Group
Recommended call:mls.point_cloud.pointcloud_assign_group(...)
Alias:PointCloud_Assign_Group
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`Parameters`	`object`	Yes	`—`	Assign group parameters

`Parameters` fields

Field	Type	Required	Default	Description
`bClearGroups`	`boolean`	No	`True`	Clear existing groups before assigning
`gap`	`number`	No	`-1.0`	Group gap distance, <= 0 means auto
`minPoints`	`integer`	No	`20`	Minimum points per group

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_assign_group_parameters()
params.bClearGroups = True

result = mls.point_cloud.pointcloud_assign_group(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
)
print(result.ok, result.output, result.message)

`pointcloud_moving_objects`

Detect moving objects from point cloud

Exact tool ID:PointCloud_Moving_Objects
Recommended call:mls.point_cloud.pointcloud_moving_objects(...)
Alias:PointCloud_Moving_Objects
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`Parameters`	`object`	Yes	`—`	Moving objects detection parameters

`Parameters` fields

Field	Type	Required	Default	Description
`fromClass`	`object`	No	`—`	Class selection for source points. A JSON object mapping selection index to classification code. Example: {"0":5} selects class 5
`toClass`	`integer`	No	`30`	Target classification code to assign to detected moving objects
`searchRadius`	`number`	No	`0.15`	Search radius
`timeDiff`	`number`	No	`0.5`	Time difference threshold
`bUseGroup`	`boolean`	No	`False`	Use group information from point cloud
`clusterRadius`	`number`	No	`0.5`	Clustering radius
`minPointsSize`	`integer`	No	`50`	Minimum cluster point count

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_moving_objects_parameters()
params.fromClass = {}

result = mls.point_cloud.pointcloud_moving_objects(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
)
print(result.ok, result.output, result.message)

`pointcloud_feature_computor`

Compute geometric features of point cloud

Exact tool ID:PointCloud_Feature_Computor
Recommended call:mls.point_cloud.pointcloud_feature_computor(...)
Alias:PointCloud_Feature_Computor
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`Parameters`	`object`	Yes	`—`	Feature computation parameters

`Parameters` fields

Field	Type	Required	Default	Description
`radius`	`number`	No	`0.1`	Search radius for feature computation
`featureSwitch`	`integer`	No	`-1`	Feature switch bitmask controlling which geometric features to compute. Bitwise OR of enum values. Example: 7 enables Roughness + Mean curvature + Gaussian curvature
`bRoughUpDir`	`boolean`	No	`False`	Use custom rough up direction
`roughUpDirX`	`number`	No	`0.0`	X component of rough up direction
`roughUpDirY`	`number`	No	`0.0`	Y component of rough up direction
`roughUpDirZ`	`number`	No	`1.0`	Z component of rough up direction

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_feature_computor_parameters()
params.radius = 0.1

result = mls.point_cloud.pointcloud_feature_computor(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
)
print(result.ok, result.output, result.message)

`pointcloud_points_deduplicator`

Remove duplicate points from point cloud

Exact tool ID:PointCloud_Points_Deduplicator
Recommended call:mls.point_cloud.pointcloud_points_deduplicator(...)
Alias:PointCloud_Points_Deduplicator
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`Parameters`	`object`	Yes	`—`	Points deduplication parameters

`Parameters` fields

Field	Type	Required	Default	Description
`fromClass`	`object`	No	`—`	Class selection for source points. A JSON object mapping selection index to classification code. Example: {"0":5} selects class 5
`gap`	`number`	No	`0.001`	Duplicate gap distance, <= 0 means auto

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_points_deduplicator_parameters()
params.fromClass = {}

result = mls.point_cloud.pointcloud_points_deduplicator(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
)
print(result.ok, result.output, result.message)

`pointcloud_adaptive_downsample`

Adaptive downsampling of point cloud

Exact tool ID:PointCloud_Adaptive_Downsample
Recommended call:mls.point_cloud.pointcloud_adaptive_downsample(...)
Alias:PointCloud_Adaptive_Downsample
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`Parameters`	`object`	Yes	`—`	Adaptive downsampling parameters

`Parameters` fields

Field	Type	Required	Default	Description
`feature`	`integer`	No	`-1`	Feature switch bitmask controlling which features to preserve during downsampling. Bitwise OR of enum values. Example: 3 enables Curvature + Intensity
`nonkeyPointVoxelSize`	`number`	No	`0.2`	Non-key point voxel size
`uniformMinRadius`	`number`	No	`0.02`	Uniform minimum radius
`maxPtsNumber`	`integer`	No	`1500000`	Maximum point count
`searchKnn`	`integer`	No	`30`	Search KNN count for feature computation
`minCurvature`	`number`	No	`0.02`	Curvature threshold
`minColorSigma`	`number`	No	`0.1`	Color variance threshold
`minIntensitySigma`	`number`	No	`0.03`	Intensity variance threshold

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_adaptive_downsample_parameters()
params.feature = -1

result = mls.point_cloud.pointcloud_adaptive_downsample(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
)
print(result.ok, result.output, result.message)

`pointcloud_density_analysis`

Density quality analysis of point cloud

Exact tool ID:PointCloud_Density_Analysis
Recommended call:mls.point_cloud.pointcloud_density_analysis(...)
Alias:PointCloud_Density_Analysis
Required parameters:input_paths, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`Parameters`	`object`	Yes	`—`	Density analysis parameters

`Parameters` fields

Field	Type	Required	Default	Description
`gridSize`	`number`	No	`1.0`	Grid size for density analysis
`reportPath`	`string`	No	`—`	Output report path in HTML format
`reportName`	`string`	No	`—`	Report name
`projectName`	`string`	No	`—`	Project name
`company`	`string`	No	`—`	Company name
`author`	`string`	No	`—`	Author name
`logoPath`	`string`	No	`—`	Logo image file path

Example

from gvscript import mls

params = mls.point_cloud.pointcloud_density_analysis_parameters()
params.gridSize = 1.0

result = mls.point_cloud.pointcloud_density_analysis(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
)
print(result.ok, result.output, result.message)

`convertprojectedsurface`

Convert point cloud to projected surface for compensation

Exact tool ID:ConvertProjectedSurface
Recommended call:mls.point_cloud.convertprojectedsurface(...)
Alias:ConvertProjectedSurface
Required parameters:input_paths, output_path, Parameters

Parameters

Parameter	Type	Required	Default	Description
`input_paths`	`array`	Yes	`—`	Input point cloud file paths
`output_path`	`string`	Yes	`''`	Output folder. If the user does not specify, set this parameter to ""
`Parameters`	`object`	Yes	`—`	Projected surface conversion parameters

`Parameters` fields

Field	Type	Required	Default	Description
`compensating`	`integer`	No	`0`	Projected surface height for compensation (-9999 to 9999)

Example

from gvscript import mls

params = mls.point_cloud.convertprojectedsurface_parameters()
params.compensating = 0

result = mls.point_cloud.convertprojectedsurface(
    Parameters=params,
    input_paths=['D:/data/input.LiData'],
    output_path='',
)
print(result.ok, result.output, result.message)

Point Cloud Processing API

Functions

pointcloud_extract_by_elevation

Parameters

Parameters fields

Example

pointcloud_extract_by_class

Parameters

Example

pointcloud_extract_by_intensity

Parameters

Parameters fields

Example

pointcloud_extract_by_return

Parameters

Example

ortho_photo

Parameters

Example

pointcloud_transform_gps_time

Parameters

Parameters fields

Example

pointcloud_compute_normals

Parameters

Parameters fields

Example

pointcloud_normalize_by_ground_points

Parameters

Parameters fields

Example

pointcloud_denormalization

Parameters

Parameters fields

Example

pointcloud_merge

Parameters

Parameters fields

Example

pointcloud_subsampling

Parameters

Parameters fields

Example

pointcloud_noise_filter

Parameters

Parameters fields

Example

pointcloud_remove_outliers

Parameters

Parameters fields

Example

pointcloud_smooth

Parameters

Example

pointcloud_brightness_contrast

Parameters

Example

pointcloud_cut_long_range

Parameters

Example

pointcloud_cutoverlap

Parameters

Example

pointcloud_assign_group

Parameters

Parameters fields

Example

pointcloud_moving_objects

Parameters

Parameters fields

Example

pointcloud_feature_computor

Parameters

Parameters fields

Example

pointcloud_points_deduplicator

Parameters

Parameters fields

Example

pointcloud_adaptive_downsample

`pointcloud_extract_by_elevation`

`Parameters` fields

`pointcloud_extract_by_class`

`pointcloud_extract_by_intensity`

`Parameters` fields

`pointcloud_extract_by_return`

`ortho_photo`

`pointcloud_transform_gps_time`

`Parameters` fields

`pointcloud_compute_normals`

`Parameters` fields

`pointcloud_normalize_by_ground_points`

`Parameters` fields

`pointcloud_denormalization`

`Parameters` fields

`pointcloud_merge`

`Parameters` fields

`pointcloud_subsampling`

`Parameters` fields

`pointcloud_noise_filter`

`Parameters` fields

`pointcloud_remove_outliers`

`Parameters` fields

`pointcloud_smooth`

`pointcloud_brightness_contrast`

`pointcloud_cut_long_range`

`pointcloud_cutoverlap`

`pointcloud_assign_group`

`Parameters` fields

`pointcloud_moving_objects`

`Parameters` fields

`pointcloud_feature_computor`

`Parameters` fields

`pointcloud_points_deduplicator`

`Parameters` fields

`pointcloud_adaptive_downsample`

`Parameters` fields

`pointcloud_density_analysis`

`Parameters` fields

`convertprojectedsurface`

`Parameters` fields