3D Coarse Matching (Multiple Models)

Instruction: The path of 3D model file, the construction process can refer to the complete point cloud model stitching document. You can enter multiple file paths. Please use semicolons to divide different file paths.

Example: Ensure that the files entered under each parameter are in the same order, meaning that the Model File has the same path order as the Geometric Center File, as shown in the figure below. Different files are separated by “;”.

Instruction: This parameter is used to adjust the number of adjacent points in the direction of the computed 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 of adjustment: 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 of adjustment: 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 15. 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.

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 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 field point cloud downsampling. It sets an upper limit for the number of points in the field point cloud after the field point cloud downsampling. If the matching effect or matching speed is not ideal, this parameter is recommended to be increased.

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 0.02.

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 Distance between Two Points = Distance Quantification × Sampling Interval, the larger the value is, the larger the distance, and the less precise the result tends to be.

Instruction: The value for the quantification of the angle between two vectors. As Angle between Two Vectors = 2 × 3.14 / Angle Quantification, 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. 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. When the parameter 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. Only results are compared here.

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

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.