3D Fine Matching

Instruction: This parameter is used to specify the method to calculate the orientations of points in the point cloud. There are four methods as shown below. Please select one according to the actual situation of the project.

Instruction: This parameter is used to set the number of points in the vicinity of the target point to search for. The minimum value is 1.

Value list: HighSpeed, Standard, and HighPrecision.

Instruction: There are three matching modes. Select the appropriate one according to the project requirements:

HighSpeed: the fastest but less accurate. Standard: relatively stable. HighPrecision: the most accurate but slower.

Instruction: Iteration refers to iteratively running a specific instruction until the conditions are met. The number of iterations refers to the number of repetitions of instruction running in this process. The parameter set here is the upper limit of the number of iterations. The larger the value is, the greater the number of times of matching calculations will be, which means a longer running time and higher accuracy.

Instruction: The value here needs to match with the initial pose deviation. When the input initial pose deviation after the previous step (3D Coarse Matching) is greater, a larger Standard Deviation is required. In this case, the matching speed is faster.

Example: When the set value is larger than the initial pose deviation, there will be a great result deviation. As shown in the figure below, the Standard Deviation on the right is 40.000 mm, far greater than the initial pose deviation. You can see that the matching result on the right with an overly large standard deviation is less accurate. Normally, this value will be no greater than 10 mm.

Instruction: During the matching process, the Standard Deviation gradually decreases until it reaches the value of Minimum Standard Deviation. The number of times that the standard deviation decreases in this process is the value of this parameter. For some cases with large standard deviation, the more times of the decrease, the more stable the entire decrease process. Under normal circumstances, this parameter does not need to be adjusted.

Instruction: It is recommended to enable this option if there is a large number of objects in the scene. If this option is enabled, the Step will perform faster when there is a large number of objects. However, if the number of objects is small, the processing speed of the Step may be slowed down.

Value range: [1.000, +∞)

Instruction: This parameter is used to adjust the search radius of the nearest point (in mm), and the value setting should correspond to the input initial pose deviation. When the input initial pose deviation is large (the deviation between the scene point cloud and the point cloud model is large), this parameter needs to be increased; when the initial pose deviation is very small, this parameter can be adjusted to a small value. The minimum value that can be set for this parameter is 1.000 mm. If the radius is less than this lower limit, some sparsely distributed points may not find the closest point, which could affect the output result.

Instruction: This parameter (Mean Square Error Threshold) is used to adjust the upper limit of the mean square error. This value is recorded after each iteration and used for comparison in subsequent iteration steps. When the mean square error of an iteration is less than the set value, it is considered that the iteration effect has met the requirements and the iteration will be terminated.

Value range: [3, +∞)

Instruction: This parameter refers to the number of consecutive iterations with small error fluctuations in the iterative optimization process. When this parameter is too small, a local optimal solution may appear, which affects the final matching accuracy. Example: If the window only contains the error of the results of three consecutive iterations, so the error trend can only be obtained by comparing the errors of the three iterations, which may lead to a local optimal solution (the error of the second iteration is less than the error of the first iteration and the first iteration) instead of a global optimal solution. According to the recommended value of the number of iterations which is 30, subsequent iterations with more significant error reduction may occur.

Instruction: If it is selected, the corresponding points will be displayed in each iteration.

Instruction: Selected if the target object is a complex-shaped object (not a simple geometric shape) or if the Automatic Weight function is required.

Instruction: If it is selected, the weight of iteration points will be calculated automatically. It is recommended to use this function for objects with poor coarse matching results.

Instruction: This parameter decides whether to remove repeated point pairs. There are multiple point pairs in the process of searching for the nearest point, and sometimes the unwanted point pairs need to be filtered out. If this option is selected, this function will be used to remove them through configurating the following N Sigma Threshold and Point Pair Angle Diff Thre parameters; if not checked, these two parameters will not take effect during the matching process.

Instruction: This parameter is the multiplier of the standard deviation threshold of the distance between each points. The point pair whose distance is outside N sigma will be eliminated.

Instruction: This parameter is the upper limit of the angle difference (in °) between the normals of two points in every matching point pair. If the angle difference is greater than this value, this point pair will be eliminated.

Instruction: If it is selected, the weight of the model will be used in the iteration of the algorithm, so it is more likely to match better.

Instruction: If it is selected, the function of removing repeating corresponding point pairs will be used, and the settings of Remove Repeat Correspond Type will take effect. It is recommended to use this function when the point cloud model has more points than that of a single object in the scene.

List of values: MinDis and GlobalMin

Instruction: This parameter is used to select the method of removing repeating corresponding point pairs. There are two methods in total:

MinDis: Calculate the distance between the repetitive point pairs. Then, keep the point pair with the smallest distance, and delete the remaining repetitive point pairs. More repeating corresponding point pairs will be eliminated in this method than the other. GlobalMin: Take the average value of the distance between the repetitive point pairs as a reference to find out the point pair whose distance is closest to the average value. Then, retain this point pair and eliminate others. Less repeating corresponding point pairs will be eliminated in this method than the other.

Instruction: This parameter is used for the downsampling of the model and scene point cloud. The bigger the interval, the less the point number of the sampled point cloud, and the lower the accuracy of model estimation.

Example: The figure below shows the output results when Sampling Interval is set to 5.000 mm and 25.000 mm, respectively. The white parts are the input object point clouds, and the colored lines are the matching results. It can be seen from the comparison of the two pictures that the smaller the sampling interval is, the more accurate the output result will be.

Value range: [0, +∞)

Instruction: The confidence threshold is used to determine whether the matching result is valid. Matching results with confidences higher than the threshold are valid and those lower are not. A higher threshold leads to fewer valid matches, while the output results are more accurate. A lower threshold leads to more valid matches, while the output results are less accurate.

Example: In the figure below, the confidence threshold of (a) is 0.500, and that of (b) is 0.920. The match of (a) is more accurate than that of (b). It can be known that when the larger Confidence Threshold is, the more likely it is that results with higher matching accuracy are retained.

Value range: [0, +∞)

Instruction: The value of Search Radius affects the pose matching score. The higher the value of the Search Radius, the looser the criterion for evaluating the pose matching result will be, which means that even if the pose matching result is of poor quality, the pose matching score will be high; the lower the value of the Search Radius, the more strict the criterion for evaluating the pose matching result will be. Please set the value according to the actual situation of the point cloud. If the points in the point cloud are sparse, the Search Radius should be set slightly larger.

The larger the value, the more matches are generated.

Instruction: The larger the value is, the more matching pairs will be output. The figure below shows the output results when this parameter is set to 1 (left) and 4 (right).

Instruction: If this option is selected, the output model and scene point cloud will be displayed.

List of values: ROTATE_BY_X, ROTATE_BY_Y, and ROTATE_BY_Z

Instruction: This parameter is used to select the symmetry rotation axis around which the initial pose is changed.

Instruction: This parameter specifies the angle of rotational symmetry.

Example: If an object looks the same before and after rotating 60° around an axis, its angle of rotational symmetry is 60°.

Instruction: This parameter is used to adjust the minimum rotation angle.

Instruction: This parameter is used to adjust the maximum rotation angle.

Default value: Disabled.

Default value: 135.000°

Instruction: This parameter is to set the weight value for the parts with salient features in the point cloud model. The initial weight of all points is 1. After setting this parameter for the target cloud, the weight coefficient of the target cloud will become 1Xthisparameter, so as to achieve the purpose of emphasis in the subsequent matching steps.

Instruction: This parameter is used to set the radius for setting weights. The unit is millimeters. The weight setting is an operation performed on the original point cloud model. The point cloud model will be down-sampled before it is used in the matching process, which causes some points to be shifted or lost. In the process of setting the weight of the points near the missing point, the search radius needs to be set, and the search radius is set by this parameter.

Instruction: If it is selected, the angle difference of the normal vector of the corresponding point pair will be considered in the pose validation process. In this case, although the output is often reduced, it could be more accurate.

Instruction: If it is selected, the matching score will be multiplied by the proportional coefficient of the corresponding point to the scene point. It is not suitable for the case of estimating poses of multiple objects in one point cloud.

Instruction: If it is selected, the sampled point cloud model will be visualized in the output.

Instruction: If it is selected, the sampled scene point cloud will be visualized in the output.

Instruction: If it is selected, the corresponding relationship between the model and the scene point cloud will be visualized in the output.