RVC::X2::CaptureOptions Struct Reference

Capture options. More...

#include <RVC.h>

Public Member Functions
	CaptureOptions ()
	Construct a new Capture Options object.

Public Attributes
CameraID	transform_to_camera
	CameraID_Left transfrom 3D points from calibration board system to left camera coordinate system CameraID_Right transfrom 3D points from calibration board system to right camera coordinate system other is do not transfrom.
int	projector_brightness
	Set the projector brightness value.
bool	calc_normal
	flag Whether calculate 3D points normal vector
unsigned int	calc_normal_radius
	Neighborhood radius in pixel of calculating normal, > 0.
int	light_contrast_threshold
	Light contrast trheshold, range in [0, 10]. The contrast of point less than this value will be treat as invalid point and be removed.
int	edge_noise_reduction_threshold
	edge control after point matching, range in [0, 10], default = 2. The big the value, the more edge noise to be removed.
int	exposure_time_2d
	Set the exposure_time 2D value in milliseconds.
int	exposure_time_3d
	Set the exposure_time 3D value in milliseconds.
float	gain_2d
	Set the 2D gain value in milliseconds.
float	gain_3d
	Set the 3D gain value in milliseconds.
int	hdr_exposure_times
	Set the hdr exposure times value. 0,2,3.
int	hdr_exposuretime_content [3]
	Set the hdr exposure time content. capture with white light, range [11, 100], others [3, 100].
float	hdr_gain_3d [3]
	Set the hdr 3D gain content.
int	hdr_projector_brightness [3]
	Set the hdr projector brightness value.
float	gamma_2d
	Set the 2D gamma value in milliseconds.
float	gamma_3d
	Set the 3D gamma value in milliseconds.
ProjectorColor	projector_color
	projector color
bool	use_projector_capturing_2d_image
	Set 2D image whether use projector.
SmoothnessLevel	smoothness
	control point map smoothness
bool	use_auto_noise_removal
	flag Whether to use auto noise removal. If true, the noise_removal_distance and noise_removal_point_number will be ignored. It will automatically adjust these two parameters according to the point cloud data and do noise removal. More...
double	noise_removal_distance
	The smaller the value, the greater the filtering degree. range: 0 ~ 20 (units: mm)
int	noise_removal_point_number
	Set the noise filter parameter 2. The larger the value, the greater the filtering degree.
double	downsample_distance
	uniform downsample distance. if <= 0, do nothing.
CaptureMode	capture_mode
	control capture mode
double	confidence_threshold
	confidence threshold, the point with confindence low then this value will be removed range: [0, 1]
int	scan_times
	scan times, only use in robust mode. range [2, 8],
int	hdr_scan_times [3]
	Set the hdr scan times, only use in robust mode. range [2, 8],.
int	bilateral_filter_kernal_size
	the kernal size of bilateral filter of depth map range: [3, 31], 0 for not use bilateral filter
double	bilateral_filter_depth_sigma
	the guassian sigma in depth of bilateral filter of depth map range: [0, 100]
double	bilateral_filter_space_sigma
	the guassian sigma in space of bilateral filter of depth map range: [0, 100]
int	line_scanner_scan_time_ms
	Set line scanner mode's total scan time.
int	line_scanner_exposure_time_us
	Set line scanner mode's single line exposure time.
int	line_scanner_min_distance
	Set line scanner mode's minimum working distance.
int	line_scanner_max_distance
	Set line scanner mode's maximum working distance.
bool	correspond2d
	whether to generate point clouds corresponding to 2D images
uint16_t	line_scanner_laser_position
	select laser position in fixed line scan mode
bool	use_auto_bilateral_filter
	flag Whether to use auto bilateral filter. If true, the bilateral_filter_kernal_size and bilateral_filter_depth_sigma and bilateral_filter_space_sigma will be ignored. It will automatically adjust these three parameters according to the point cloud data and do bilateral filter.
int	reflection_filter_threshold
	Used to remove large-area erroneous point clouds caused by reflections. The higher this value is set, the more points will be removed by the reflection filter. Setting it too large may remove data from thin and pointy objects. range:[0, 30].
double	smooth_sigma
	The Gaussian filters are used to smooth points. The larger the smooth_sigma, the smoother the point cloud becomes. However, an excessively large smooth_sigma can cause edges to be smoothed out as well. range: [0, 5].
RVC::ROI	roi
	set ROI function, only 3d points in the roi will be generated. This function can improve 3d capture speed and only support SwingLineScan capture mode. The parameters of roi must comply with the rules of the equipment and can be checked by CheckRoi. We recommend that you adjust roi automatically through C before AutoAdjustRoi.
bool	enable_2d_in_capture
	Acquire 2D images during capture.
bool	pointcloud_completion
	In 'CaptureMode_SwingLineScan' mode with 'correspond2d' enabled,turning on 'pointcloud_completion' will interpolate the point cloud to produce a dense reconstruction.
int	line_scanner_brightness_threshold
	Only laser lines with brightness greater than the 'line_scanner_brightness_threshold' will be extracted by the algorithm. A higher threshold results in fewer extracted points, while a lower threshold yields more points but may introduce additional noise. When scanning black objects, a lower threshold can be used. More...

Detailed Description

Capture options.

Member Data Documentation

line_scanner_brightness_threshold

int RVC::X2::CaptureOptions::line_scanner_brightness_threshold

Only laser lines with brightness greater than the 'line_scanner_brightness_threshold' will be extracted by the algorithm. A higher threshold results in fewer extracted points, while a lower threshold yields more points but may introduce additional noise. When scanning black objects, a lower threshold can be used.

range: [0, 200]

default: 5

use_auto_noise_removal

bool RVC::X2::CaptureOptions::use_auto_noise_removal

flag Whether to use auto noise removal. If true, the noise_removal_distance and noise_removal_point_number will be ignored. It will automatically adjust these two parameters according to the point cloud data and do noise removal.

Point cloud noise removal algorithm, contains three parameters: use_auto_noise_removal, noise_removal_distance and noise_removal_point_number. This algorithm performs noise reduction through a clustering algorithm. The point cloud is classified into blocks according to the distance (noise_removal_distance). If the total number of points in the block is less than a certain threshold (noise_removal_point_number), the points in the block are considered as noise points and need to be removed.

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The documentation for this struct was generated from the following file:

• RVC.h