Settings

Common setting

Common setting can be found in Preference > Algorithm (common)

Location	Field	Name	Description	Unit	Default	Range
Common	Output	Publish Point-Cloud	The level of publishing point cloud to output port. This setting will greatly increase the bandwidth between the algo nodes and the master node! 0: Nothing published 1: Only object point published 2: Both object & background points published	int	2
		Point-Cloud Bandwidth Reduction	This decreases the communication burden by reducing pointcloud data size. This option affects the update method which sequentially updates each lidar is in calibration mode and downsample background and ground points in runtime mode.	boolean	false
		Downsampling Resolution	Set resolution of downsampling method of pointcloud bandwidth reduction. Value can be between 0 to 1.	Float	0.3
		Point-Cloud Update Interval	Time between updates of background point cloud information from algo nodes to the master node. Updating frequently can be resource-intensive..	seconds	1.0
		Sensor Update Interval	Time between updates of sensor information from algo nodes to the master node.	seconds	0.1
		Output Sending Type Selection	Method for sending and updating new results. 0 - Instant Sending: Publish result whenever a new one is available. 1 - Fixed-Period Sending: Publish the most recent result periodically. The published result might already have been published in a previous frame. 2 - Fixed-Period with Max-Delay: Publish the most recent result periodically. If there is no new result, the sending is postponed until a new result is received or a maximum delay time has passed.	int	1
		Update Interval	Publish interval for output sending type 1 - Fixed-Period Sending and type 2 - Fixed-Period with Max-Delay	seconds	0.1
		Max Delay	Maximum delay of output sending for the output sending type 2 - Fixed-Period with Max-Delay	seconds	0.05
		Publish Point-Cloud Intensity	Enable this option to publish Point Intensity values to Master Node. This option can be disabled when intensity visualization is not being used.	True/ False	False
		Trigger Zone Events with MISC Objects	Enabling this will trigger zone events with MISC objects	True/ False	False
	Pipeline Cycle	Maximum Lidar Input Interval	If there is no response from one of the lidar sensors for this time duration, that lidar sensor will be considered dead.	seconds	4	[0,30]
		Restart dead lidar drivers automatically	When this is enabled, lidar sensor driver will be restarted automatically if it does not response for duration specified in “Maximum Lidar Input Interval”	True/ False	True
		Frame Update Method	Method for updating new frames from received results 0- Instant: Update frames as soon as results arrive 1 - Use buffer: Update frames using buffer that stores frames after specified delay time in case of network traffic	Int	0
		Minimum Pending Time	This option is only visible if Instant is selected in the Frame Update Method. Due to connection issues, the time interval between two point-cloud messages of one lidar can be unreasonably small. This minimum interval will ensure that one lidar point-cloud message of the pipeline has a reasonable time gap with the next one. A message which time difference with the previous message is below this minimum interval will get discarded.	seconds	20	[0, 100.]
		Maximum Pending Time	This option is only visible if Instant is selected in the Frame Update Method. Perception pipeline will be triggered whenever data from all connected lidar sensors were received or at least new data from one lidar sensor arrived and this time threshold is reached.	seconds	150	[100, 1000]
		Processing Delay Time	This option is only visible if the “Use buffer” option is selected in the Frame Update Method. All received frames will be stored in the buffer and visualized after this time. All received frames will be results from this time ago.	seconds	0.1	[0.0, 3.0]
		Frame Frequency	This option is only visible if Use buffer is selected in the Frame Update Method. Frames from the buffer will be processed in this specified frequency cycle.	seconds	0.1	[0, +inf]
	Tracking History	Tracking History > Length	The number of history positions to keep.	int	100	[0, +Inf.]
		Tracking History > History Smoothing Level	Trajectory smoothing level	int	3	[0, 3]
	Trajectory Prediction	Trajectory Prediction> Number of Prediction Steps	Number of prediction steps	int	10	[0, +Inf.]
		Trajectory Prediction> Prediction Step Time-Horizon	Time step between two predicted points	seconds	0.1	[0, +Inf.]
		Trajectory Prediction> Max. Acceleration Filter Threshold	Maximum acceleration for prediction	m/s2	15.0	[0, +Inf.]

Master setting

Master setting can be found in Preference > Algorithm (master)

Location	Name	Description	Unit	Default	Range
Output-Merger > Algo-Node Result Timeouts	Algo-Node Object Result-Timeout	If a point cloud receival time of a result from an algo node and the latest receival time differ by more than this timeout, the algo node result is removed from result aggregation.For the point result, the point-cloud update frequency is added to this timeout. For the sensor statuses, the sensor update frequency is added to this timeout.	seconds	0.2	[0.1, +Inf.]
	Algo-Node Point Result Timeout		seconds	0.2	[0.1, +Inf.]
	Algo-Node Sensor Status Timeout		seconds	0.2	[0.1, +Inf.]
Output-Merger > Multi Algo-Node Merger	Dominant Algo-Result Criterion	Method for deciding the dominant algo-result. 0 - Largest BBox: The object with the largest bbox is regarded as the dominant algo-result. 1 - Largest Bbox and Longest Track: If there’s a significantly large bbox, the algo-result takes dominance. If not, an algo-result with longer tracking history takes dominance.		0	[0, 1]
	Scale Ratio Threshold for Age Comparison	Scale ratio threshold for 1 - Largest Bbox and Longest Track. Tracking period takes priority for the dominance when there's a size ambiguity between algo-nodes. The size ambiguity condition is defined below: abs(max_size - size) < thr * max_size		0.1	[0.01, 0.5]
	Overlap-Region Map Resolution	Resolution of overlap region map	meters	5.0	[0.1, +Inf.]
	Max-Box-Distance to Split	Max distance to split over merged objects	meters	0.75	[0, +Inf.]
	Grow-Size x-Tolerance	x-size increasing tolerance to control objects association	meters	2.0	[0, +Inf.]
	Grow-Size y-Tolerance	y-size increasing tolerance to control objects association	meters	1.5	[0, +Inf.]
	Grow-Size Rate Tolerance	size ratio tolerance to control objects association		1.2	[0, +Inf.]
	Grow-Size Addition-Tolerance	size addition tolerance to control objects association	meters	0.5	[0, +Inf.]
	Association Distance Threshold	Distance threshold to associate objects in two nodes	meters	2.0	[0, +Inf.]

Algorithm setting

To change algorithm settings you can open the algorithm window (with the Preference > Algorithm (ip_address) in the menu bar). A description of the parameter specification and a more detail of how to tune the parameter can be seen below. Be advised to change algorithm parameters with caution, as wrong parameters can negatively affect the performance.

Location	Field	Name	Description	Unit	Range
Parameters > Object		Allow Floating-Object	If True, min. z of the object is set to be the min. z-value of the object bounding box instead of ground height.	True/ False
		Box Orientation Resolution	Object bounding box orientation resolution.	degrees	[3.75, 15.0]
Parameters > Sanity-Checks	Tracked Object	Max-Dimensions (W, L)	The maximum object size which can be tracked.	meters	[0, +Inf.]
Parameters > Tracking		Min. Points for Tracking	The minimum number of points of an object to be tracked.	-
		Validation Period	Period of checking validity in the early stage of tracking. This time determines length of the VALIDATION period, or how quickly a new object will be tracked and classified	seconds	[0, +Inf.]
		Invalidation Period	Period of short term prediction when tracking is lost while in the VALIDATION period. A longer period will allow for objects that are at the edge of detection range to be tracked, but will be more likely to introduce false alarms.	seconds	[0, +Inf.]
		Drifting Period	Period of short term location prediction when tracking is lost in TRACKING status. A longer period can help with obstructions, but can lead to object ID switching in busy environments	seconds	[0, +Inf.]
Pipeline > Ground Detector	Elevation Ground Detector	Max. Ground Height	Maximum height for ground segmentation.	meters	[-0.5, +Inf.]
Pipeline > Background Detector	Background Detector	Apply	If True, use the background detector.	True/ False
		Accumulation Frame Count	Number of accumulated frames to detect background points .	-	[1, 20]
		Time to Initialize Background	Initiated period to learn background points.	seconds	[0, +Inf.]
		Time to Become Background	The period for static objects becomes background.	seconds	[1, +Inf.]
		Time to Become Foreground	The period for a background object becomes foreground when it starts moving.	seconds	[1, +Inf.]
		Background Reset Rate	Changing rate from foreground to background when no point is detected		[0, +Inf.]
		Use Multi-lidar Background Fusion	Fuse background information from multi-lidar	True/False
		Multi-lidar Background Fusion Time Threshold	The maximum period which allows the fusing of multi-lidar background information.	seconds	[1, +inf]
		Number of Frames To Estimate Detection Range	Number of initial frames used to estimate the background detection range		[1, +Inf]
		Use Global Detection Range	If use this option, the global detection range will be used for background detection range instead of using initial frames estimation	True/False
		Range Margin	Margin to build static map to classify background	meters	[0, +inf.]
		Angular Margin	Margin to build static map to classify background	degree	[0, 180]
		Resolutions (Range, Azimuth, Elevation)	Range (distance), Azimuth angle and Elevation angle resolution.	meters, degree, degree	[0, +Inf]
Pipeline > Clusterer	Grid-Clusterer	x-Resolution	Grid resolution in x-axis.	meters	(0, +Inf.]
		y-Resolution	Grid resolution in y-axis.	meters	(0, +Inf]
		Cell Point Threshold	Minimum points per cell for clustering		[1, +Inf.]
		Cluster Point Threshold	Minimum points per cluster to detect
Pipeline > Tracker	Hybrid-Tracker	Apply	If True, use Hybrid-Tracker	True/ False
		Association-Distance	Distance threshold to associate objects in two consecutive frames.	meters	[0.01, +Inf]
		Min. Object Radius	Minimum object radius to be tracked.	meters	[0, +Inf]
		Max. Hole-Size	Maximum distance of objects which can be merged into one object.	meters	[0, +Inf]
		Merge Object Level	Object merging level to decide how easily small closed objects can be merged into one object.	-	[0, 10]
		Max. Grow-Size Tolerance	Maximum size of merged object can grow	meters	[1, +inf]
		Max. Hole Size Thres. to Enlarge Object	Maximum a hole inside merged object can have	meters	[1.5, +Inf]
		Object-Size Shrinking-Rate	Shrinking rate of tracked object size. In case of occlusion, the size of a tracked object will be learnt and kept with this shrinking rate.	-	[0, 1]
		Use Object Splitter	Apply splitter check to over merger objects	True
		Splitting Distance Threshold	Distance threshold to split objects	metters	[0, +Inf.]
		Splitting Min. Trajectory Distance	Minimum trajectory distance to split objects	metters	[0, +Inf.]
		Splitting Min. Object Lifetime	Minimum lifetime of objects to apply splitter check	seconds	[0, +Inf.]
	Human-Tracker	Apply	If True, use Human-Tracker	True/ False
		Association-Distance	Distance threshold to associate objects in two consecutive frames.	meters	[0.01, +Inf]
		Min. Object Radius	Minimum object radius to be tracked.	meters	[0.0, +Inf]
		Merge/Split Size Mul. Threshold	Size ratio threshold that allows to merge/split		[0.0, +Inf]
		Merge/Split Size Add. Threshold	Size increasing/decreasing threshold that allows to merge/split	meters	[0.0, +Inf]
		Min. Time for Splitting	Minimum lifetime of objects to apply merger/ splitter check	seconds	[0.0, +Inf]
	GPU Pipeline Supported Tracker (GPU plug-in)	Apply	If True, use GPU Pipeline Supported Tracker	True/ False
		Tracking Association Distance	Distance threshold to associate objects in two consecutive frames.	meter	[0.0, +Inf]
		Track Miscellaneous Objects	Enable tracking of MISC objects	True/ False
		Use filter-based Prediction	Use of adaptive filter-based prediction instead of history based prediction	True/ False
		Merge Object Level	Determines the merge object level. 0: Cars only, 1: Cars and Small Objects, 2: All Objects.
		Merge/Split Size Mul. Threshold	Maximum scale up ratio of merged objects		[1.0, +inf.]
		Merge/Split Size Add. Threshold	Maximum additional size of merged objects	meters	[0.0, +inf.]
		Merge/Split Min Time Threshold	Minimum lifetime of objects to apply splitter/merging check	seconds	[0.0, +inf]
		Max Size To Apply Merging	Maximum size of objects to apply merging check	meters	[0.0, +inf.]
		Min Radius For Tracking	Minimum radius of objects to be tracked	meters	[0.0, +inf.]
		Box Shrinking Rate	Shrinking rate of tracked object size. In case of occlusion, the size of a tracked object will be learnt and kept with this shrinking rate.	-	[0.0, +inf.]
		Misc. Fused Object Reduce Score	In case of misclassification the, the confident score of the object will be learnt and kept with this reduced score.	-	[0.0, +inf.]
		Yaw Update Rate	Smoothing bounding box direction rate		[0.0, +inf.]
		Radius Offset Thresh. To Associate	Maximum radius difference to associate objects in consecutive frames.	meters	[0.0, +inf.]
		Min Speed to Aligned Box Yaw	MInimum speed to apply box direction alignment	m/s	[0.0, +inf.]
		Max Radius To Aligned Box Yaw	Maximum object radius to apply box direction alignment	meters	[0.0, +inf.]
		Min Time To Estimate Velocity	Minimum time to apply velocity estimation	seconds	[0.0, +inf.]
		Time Duration To Estimate Velocity	Time duration to apply velocity estimation	seconds	[0.0, +inf.]
		Moving Distance To Estimate Velocity	Moving Distance to apply velocity estimation	meters	[0.0, +inf.]
		Min Size For Splitting	Minimum object size to apply splitting check	meters	[0.0, +inf.]
		DL Box Margin	Maximum margin allow object to merge to a DL box	meters	[0.0, +inf.]
		Use Splitting	Unable splitting	True/ False
Pipeline > Classifier	Skipped Classification Classes	CAR/PED/CYC/MISC	Any class marked with this option will be ignored by the classifier.	True/ False
	Target Classification Classes	CAR/PED/CYC/MISC	The classes the output of the classifier will include. E.g. if only PED(estrian) and MISC(ellaneous) are chosen all the objects will be classified as either PED or MISC.	True/ False
	Only Classify Tracked Objects		If selected, only objects with tracking status will be classified	True/False
	First Order Classifier		This session will be applied first and only if it can’t classify the objects the send order classifier can assist it.
	Min. Dominant Score		Min class score to become dominant class. If a tracked object is classified as a certain class (CAR, PED, CYC) with score > Min. Dominant Score for more than Accum. Dominant Thresh. times then it becomes dominant and less likely changes the class label unless other class becomes dominant.		[0, 1]
	Accum. Dominant Thresh.				[0, +inf]
	Size Classifier for Big Objects	Apply	If True, use the size classifier for big objects. If used, all objects which have size bigger than Min Length, Width, Height will be classified as one of Include Classes.	True/ False
		Min Length, Width, Height	Min length, width, height to classify as big objects.	meters	[0, +Inf]
		Min. Big Object Offset	The offset used to estimate the score of big objects.	metter	[0, +Inf]
		Include Classes	The classes the output of the size classifier for big objects will include.	True/ False
	Size Classifier for Small Objects	Apply	If True, use the size classifier for small objects. If used, all objects which have size in the Length Range, Width Range, Height Range will be classified as one of Include Classes. If an object is classified as a big object, it won’t be classified as a small object even though it’s in the small object range.	True/ False
		Length Range/ Width Range/ Height Range	The range of length, width, height to classify as small objects.	meters	[0, +Inf]
		Include Classes	If True, the classes the output of the size classifier for big objects will include.	True/ False
	Velocity Classifier	Apply	If True, use the velocity classifier.	True/ False
		Non-MISC Min Velocity	Minimum velocity which object will not be classified as a MISC.	meters	[0, +Inf]
		Non-MISC Min Displacement	Minimum displacement which object will not be classified as a MISC.	meters	[0, +Inf]
		PED Max Velocity	Minimum velocity which object will not be classified as a PED.	km/h	[0, +Inf]
		Use Velocity Estimator	Enable this to use the velocity estimator	True/ False
		Min Time to Update Vel. Estimator	Minimum time required to update the velocity estimator	seconds	[0, +inf]
		Min Distance to Update Vel. Estimator	Minimum distance required to update the velocity estimator	meters	[0, +inf]
		Min Time to Estimate Velocity	Minimum time required to estimate the velocity	seconds	[0, +inf]
		Min Distance to Estimate Velocity	Minimum distance required to estimate the velocity	meters	[0, +inf]
	ML Classifier	Apply	If True, use the machine learning classifier. If applied, it will be used only after using size classifier and velocity classifier. E.g, After the size classifier and velocity classifier, the object is classified as either PED or CYC, ML classifier will decide whether it’s a PED or a CYC.	True/ False
		Min Num Points	Minimum number of points to use ML classifier.		[0, +inf]
		Max Num Objects To Classify	Maximum number of objects can be classified by ML classier.		[0, +inf]
Pipeline > Cluster Merger(GPU plug-in)	Nearest Cluster Merger	Apply	Enable Nearest Cluster Merger	True/ False
		Box Merging Type	Set the type of box merging. 0: Use bounding boxes from deep learning 1: Use tight bounding boxes 2: use deep learning base point with tight bounding box
		Similar Size Offset Threshold	Offset threshold for clusters of similar size	meters	[0.0, +inf]
		Suppress DL Box Which Can't Cover Intersected Clusters	Enabling this suppresses deep learning boxes that can’t cover intersected clusters	True/ False
		Not Covered Box Radius Thresh. To Suppress DL Box	Box radius threshold for suppress deep learning boxes	meters	[0.0, +inf]
	Point-Based Cluster Merger	Apply	Enable Point-Based Cluster Merger	True/ False
Pipeline > Object Detector (GPU plug-in)	2D Object Detector	Apply	If True, enables the 2D Object Detector	True/ False
		Max Box Length	Maximum length of the bounding box	meters
		Car Probability Threshold	Minimum probability to be considered CAR		[0.0, 1.0]
		Pedestrian Probability Threshold	Minimum probability to be considered PED		[0.0, 1.0]
		Cyclist Probability Threshold	Minimum probability to be considered CYC		[0.0, 1.0]
		Exclude Invalid Points	If selected, points in an exclusion zone will not be included in the object detection model.	True/False

More on algorithm setting

Detection range

It is recommended to change some parameters to better fit each specific use case. As mentioned in previous sections, detection range is one of the important parameters that should be tuned carefully to save computational resources, reduce noise and false detections (prefered to Sensor set up, Optimizing the world size). If you want to change detection range while in runtime mode, go back to sensor setup mode (with the Mode > Sensor Setup in the menu bar) and click the Algo Node Settings gear icon.

Tracked Object Max-Dimensions

Tracked Object Max-Dimensions (W, L) need to be bigger than the maximum size of objects you want to track and classify in the scene but don’t set it too big compared to that size.

Background Detector and Zone

Background detector is used to automatically set exclusion zones (called background zones) to ignore static objects in the detection pipeline. If an object stays still for more than Time to Become background seconds, the background detector will set a background zone around that object. In consequence, that object will become background and won’t be tracked. If a background object moves away from its background zone, it needs Time to Become Foreground seconds so that the zone is removed. Since lidar is noisy and some objects can vibrate (vegetations, flags, ...), the background detector can’t completely remove all the static objects. It’s recommended to use exclusion zones (prefered to exclusion zones) for the region which you don’t want to cover.

If you also want tracked objects which can be static for longtime (e.g. track cars in the parking lot), you could disable Background Detector and use only exclusion zones instead.

Tracker

Human-tracker is more optimized for human only tracking applications. If lidar is used in the scene which doesn’t have cars and cyclists (e.g. indoor), human-tracker is recommended. If lidar is used outdoors (which have cars/cyclists), Hybrid-Tracker would perform better.

Since lidar point-clouds are sparse, a big car can break into multiple small objects. Hybrid-Tracker can merge those small objects together. The Max. Hole-Size and Merge Object Level parameters help to control this merging process. Max. Hole-Size is the maximum distance of those small objects which can be merged while Merge Object Level indicates how easy the merge process is. If lidars are used to track big objects (big trucks/containers) and there aren't many people, increasing Max. Hole-Size and Merge Object Level can help to detect the big object better. But if the scene is a crowded intersection with many pedestrians those values need to be kept small to avoid merging closed distance pedestrians to one object. Merge Object Level can have value in the range [0, 10]. We recommend using Merge Object Level 3-4 for crowded intersections and 7-8 for highways with many big trucks cases.

Classifier

Use Target Classification Classes can reduce false classification. E.g. If lidars are used on highways to detect cars/cyclists, it recommends choosing CAR, CYC, MISC as target classes. And if lidars are used indoors, choosing PED, MISC as target classes could perform better.

If only one target class is selected, then all objects are classified as the selected class without considering properties of the objects (i.e. the classification is skipped). In the case that no target class is selected, all objects are classified as MISC.

First order classifier (size based and velocity based) will be used first, and only if it can’t decide which class the object belongs to the second order classifier (machine learning based) will be applied. Let’s consider the following classifier setting and an object of size (4.5m, 2.7m, 1.5m).

The object is classified as either CAR or MISC by Size Classifier for big Objects. If it moves at a speed 20km/h, it is not a MISC or a PED by Velocity Classifier. So it will be classified as a CAR. In case it’s not moving, ML Classifier will be used to determine if it’s a CAR or a MISC.

Dealing with limited computational resources

If running with multiple lidars or using a computational-resource-limited machine, you can improve the speed of the algorithm by:

• Optimize the detection range (prefered to common setting).
• Increase resolution values in Pipeline > Clusterer. Resolution 0.1 x 0.1 means that points in a grid of size 0.1 x 0.1 will be grouped in one object. Increasing those values can help the clusterer run faster with the trade off that closed objects can be merged together.
• Increase resolution values in Pipeline > Background Detector > Range, Azimuth, Elevation. Increasing those values can help the Background Detector run faster with the trade off that objects which are closed to background can become background.

Rendering Config Editor

Rendering setting can be found in Preference > Visualizer (or press F11)

Location	Name	Description	Default
Window	Default Window-Width	Initial width of the application window.	1080
	Default Window-Height	Initial height of the application window.	720
Visibility	Object Points	Enable or disable showing points belonging to objects.	True
	Ground Point	Enable or disable showing ground points. (e.g. floor.)	True
	Background Points	Enable or disable showing background points. (e.g. wall or desk…)	True
	Draw Intensity Visualization	Enable this to color point-cloud by intensity values. The 'Publish Point-Cloud Intensity' option in Algorithm (Common) section must be turned on for this to work.	False
	Min/Max Point Intensity Range	Color range for intensity visualization. Intensity values outside of range will be clamped to the range.	[0.0, 255.0]
	Grid	Enable or disable showing squared Grid.	True
	Grid Circular	Enable or disable showing circular Grid.	False
	Axis	Enable or disable showing the world XYZ coordinate axis.	True
	Objects	Enable or disable showing tracked objects. (e.g. pedestrian)	True
	Misc Objects	Enable or disable showing miscellaneous objects.	True
	Validating Objects	Enable or disable showing validating objects.	True
	Drifting Objects	Enable or disable showing drifting objects.	True
	Invalidating Objects	Enable or disable showing invalidating objects.	True
	Object Trail	Enable or disable showing the object trail.	True
	Predicted Trajectory	Enable or disable showing the predicted trajectory of moving objects.	False
	Map Image	Enable or disable showing a map image.	True
	Detection Range	Enable or disable showing the detection range of each algo node.	True
	Points Outside of Detection Range	Enable or disable showing points outside of the detection range.	False
	Lidar Name	Enable or disable showing the name of each sensor.	False
	Lidar Topic	Enable or disable showing the topic of each sensor.	False
	Algo Node Name	Enable or disable showing the name of each algo node.	False
	Zone Name	Enable or disable showing the name of each zone.	False
	Draw Annotations	Enable or disable drawing object annotations	False
	Visible Annotations -> Show Object ID	If object annotations are enabled the object’s ID is shown	True
	Visible Annotations -> Show Object Class and Probability	If object annotations are enabled the object’s class and probability score of that class is shown	False
	Visible Annotations -> Show Object Speed	If object annotations are enabled the object’s speed is shown	True
	Visible Annotations -> Show Object Height	If object annotations are enabled the object’s height is shown	False
Color	Background Color	Color of the background.	-
	Ground Color	Color of the ground points.	-
	Background Color	Color of the background points.	-
	Object Color	Color of points belonging to objects.	-
	Car Color	Color of cars.	-
	Pedestrian Color	Color of pedestrians.	-
	Cyclist Color	Color of cyclists.	-
	Misc Color	Color of miscellaneous objects.	-
	Event Zone Color	Color of event zones.	-
	Exclusion Zone Color	Color of exclusion zones.	-
	Detection Range Color	Color of the detection range outline.	-
View Range	Min point height	Minimum height to show points (Unit: meters) The point lower than this value will not be shown even if you enabled Env. Points ON.	-10.0
	Max point height	Maximum height to show points (Unit: meters) The point higher than this value will not be shown even if you enabled Env. Points ON.	10.0
Misc	Cloud Point Size	Control the visual size of the rendered points. The values can range from 1.0 to 10.0.	1.0
	Use Random Obj. Color	This option colors each object in a different color.	False