3D Coarse Matching

Instruction: This parameter is used to set the number of adjacent points in the normal direction of the point, which is the value of K in StandardMode mode.

Instruction: This parameter is used to adjust the algorithm speed. When the value is increased, the algorithm speed becomes faster, but the matching accuracy decreases. Its effect is more obvious than Secondary Speed Controller. The valid range of this parameter is 1–6.

Example: As shown in the figure below. The left figure shows the result when this value is 2 , and the right figure shows the result when this value is 6. It is obvious that the matching accuracy decreases after adjustment.

Instruction: This parameter is used to adjust the algorithm speed. When the value is increased, the algorithm speed becomes faster, but the matching accuracy decreases. Its effect is weaker than Main Speed Controller. The valid range of this parameter is 1–20.

Example: As shown in the figure below. The figure on the left shows the result when this value is 10, and the figure on the right shows the result when this value is 18. It can be seen that the matching accuracy decreases after adjustment, but the influence is less than that of the main speed control parameters.

Instruction: This parameter is used to estimate the number of matching outputs per point cloud. The larger the value, the more matches are generated. The valid range of this parameter is from 1 to ∞.

Example of adjustment: As shown in the figure below. The left picture shows the result when the parameter is 1, and the right picture shows the result when the parameter is 3.

Instruction: This parameter is used to determine whether to use automatic downsampling. If it is selected, the sampling interval parameter of point cloud template will be automatically adjusted according to the expected points of the model after sampling.

Instruction: This parameter is used to adjust the number of points of the sampling point cloud. It is effective when the value of Enable Automatic Downsampling is selected, and the number of points of the point cloud is close to this value. The smaller this value is, the fewer points of sampling point cloud are, resulting in the lower accuracy of pose estimation.

Instruction: This parameter is used to set the maximum number of points in the point cloud model after downsampling. It sets an upper limit for the number of points in our point cloud model after downsampling. If the matching effect or matching speed is not ideal, this parameter is recommended to be increased.

Instruction: This parameter is used to set the maximum number of points in the point cloud after the scene point cloud downsampling. It sets an upper limit for the number of points in the scene point cloud after the scene point cloud downsampling, thus limiting the time spent on running. The value of this parameter cannot be lower than the actual number of points sampled. If the maximum time spent on running needs to be limited, it is recommended to decrease the value of this parameter.

Instruction: This parameter is used to adjust the maximum distance between points in the sampling point cloud. The unit is millimeters. When the sampling interval of point cloud model is smaller than the minimum sampling interval, the minimum sampling interval is used as the actual sampling interval. The larger the value is, the less point clouds are used for calculation after sampling, the lower the matching accuracy and the lower the algorithm execution time.

Example of adjustment: As shown in the figure below. The left picture shows the result when the parameter is 0.01, and the right picture shows the result when the parameter is 0.04.

Instruction: This parameter is used to calculate the sampling interval. The unit is millimeters. It is effective when the value of Enable Automatic Downsampling is selected. If the calculated sampling interval is smaller than this value, this value will be used as the actual sampling interval.

Instruction: The value for the quantification of the distance between points. As DistanceInterval = DistanceQuantification × SamplingInterval, the bigger the value is, the less precise the result tends to be.

Instruction: The value for the quantification of the angle between normals of points. As AngleInterval = 2 × 3.14 ÷ AngleQuantification, increasing the parameter’s value will reduce the matching accuracy.

Instruction: This parameter sets the threshold for the proportion of the number of votes to the maximum number of votes. The number of votes corresponding to each pose will be obtained in the previous steps, and the maximum number of votes multiplied by this parameter will get a threshold. When the number of votes of a pose is greater than this threshold, the corresponding pose will be retained for clustering operation. The smaller the value, the more likely it is to find an accurate match, but the running time increases. The valid range of this parameter is 0–1.

Instruction: This parameter is used to adjust the selection step of the reference point. The step size is taken as an interval sampling point from the point cloud. When the value is larger, the interval sampling points are fewer, and the execution speed is faster, but the matching accuracy is reduced.

Instruction: This parameter is used to adjust the selection step of the referred point. The step size is taken as an interval sampling point from the point cloud. When the value is larger, the interval sampling points are fewer, and the execution speed is faster, but the matching accuracy is reduced.

Instruction: This parameter is used to adjust the proportion of the number of poses used for clustering to the total computed poses. Any pose will be given a score during the calculation, and all poses will be sorted according to the score. This parameter determines how much of the pose is used for clustering. A value of 0.1 means that the top 10% pose is taken as the pose for clustering. The larger the value, the more likely it is to find an accurate match, but the running time increases accordingly.

Instruction: This parameter is used to adjust the size of the Angle increment in the clustering process. In the final calculation result, the same object may calculate multiple poses, which determines the increment of the Angle parameter when the poses with very close parameters are fused. The larger the parameter is, the pose with large Angle difference will be fused into the final result, and the matching accuracy will decrease.

Instruction: This parameter is used to adjust the size of the Distance increment in the clustering process. The unit is millimeters. In the final calculation result, the same object may calculate multiple poses, which determines the increment of the Distance parameter when the poses with very close parameters are fused. The larger the parameter is, the pose with large Angle difference will be fused into the final result, and the matching accuracy will decrease.

Instruction: This parameter is used to take the top N results with the highest score from the multiple matching results obtained after clustering adjustment as the final result.

Instruction: This parameter determines whether pose validation is used. If it is selected, all cluster parameters are invalid. Pose validation and clustering are two different methods for verification and screening of final matching results, which cannot be used simultaneously.

Instruction: This parameter is used to control the size of the verification area during pose verification. A single voxel is a unit. When the value is increased, the mark area used to verify the pose becomes larger, and more points are included to verify the final result, thus reducing the matching accuracy.

Instruction: The space where the point cloud is located is divided into a 3D grid, and the parameter is the size of the smallest unit of the 3D grid. When the value is increased, the box selection range becomes larger and there are more selected points for pose verification. In this case, the algorithm speed becomes faster, but the matching accuracy decreases.

Instruction: For SurfaceMatching algorithm, this parameter has the same effect as for SurfaceMatchingEasyMode algorithm.

Example of adjustment: The left side of the figure below is the result when the parameter value is 1, and the right side is the result when the parameter value is 3.

Instruction: This parameter is used to display the downsampled point cloud model.

Instruction: This parameter is used to display the downsampled field point cloud.

Instruction: This parameter is used to display the matched model and field point cloud.