3D Fine Matching (Multiple Models)

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

Example: Ensure that the order of the file paths is consistent, i.e., if the file path of an object with front side facing up is entered first in the Model Label File, the corresponding front side object file path should also be entered before the back side one in both Model File and Geometric Center File, as shown below.

After inputting file paths correctly, the output point cloud image is shown as below. Both the front and back point cloud models of the object can match the scene point cloud well.

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: GMM and nearest-neighbor.

Instruction: This parameter is used to select the local matching method to match the point cloud model with the scene point cloud. The point cloud model will move and rotate according to the set range to match the point cloud in the scene obtained in the previous step match.

Example: In most scenarios, GMM has better anti-interference ability and faster matching speed, so it is recommended to use this mode; in rare cases when GMM cannot meet the demand, the nearest point (nearest-neighbor) mode is worth trying.

Value list: HighSpeed, Standard, and HighPrecision.

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

Instruction: Iteration is the process of cyclically running for a specific instruction until the conditions are met. Number of Iterations is the number of repeated instructions in this process. The parameter set here is the maximum number of iterations. The larger this value, the more matching calculation times and the longer running time. However, larger Number of Iterations also gives better matching 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: The standard deviation should correspond to the deviation of the initial pose. If the set value is greater than the deviation of the initial pose, the accuracy of the output result will be greatly reduced. Normally, the value of this parameter setting will not exceed 0.01 m.

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: the minimum Standard Deviation, namely, the reached minimum value after the decrease process.

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: Iteration is the process of cyclically running for a specific instruction until the conditions are met. Number of Iterations is the number of repeated instructions in this process. In the Nearest-Neighbor Mode, the iteration will stop when the calculation can satisfy the requirement of algorithm accuracy. The parameter set here is the maximum number of iterations. The larger this value, the more matching calculation times and the longer running time. However, larger Number of Iterations also gives better matching accuracy.

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 0.001. 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.

Example: When the initial pose deviation is very large, and this parameter is set as the minimum value 0.001, the matching result will be incomplete, as shown by the (b) in the figure below. When this parameter is increased to 0.01, the search radius will become larger, and a complete matching result can be obtained, as shown by (a) in the figure below.

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.

Instruction: This parameter refers to the number of consecutive iterations with small error fluctuations in the iterative optimization process, which can take a minimum value of 3. 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 Remove Repeat Correspond Type parameter will be available and could take effect during the matching process. 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 unit is millimeters. The bigger the interval, the less the point number of the sampled point cloud, and the lower the accuracy of model estimation.

Example: The left and right figures below are the output results when this parameter is set as 0.0128 and 0.01, respectively. The white one is the input scene point cloud. 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.

Default value: Disabled.

Default value: 135.000°

Value range: [0, +∞)

Instruction: If the validation score of the result is larger than this threshold (which cannot be negative), the result is regarded as valid. The higher the value is, the more accurate results are retained.

Value range: [0, +∞)

Instruction: This parameter is the search radius when calculating the confidence. The unit is millimeters. 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 is, the more matches are generated.

The figure below shows the output result of the parameter when the value is set to 1.

The figure below shows the output when the parameter is adjusted to 4.

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 is used for changing the initial orientation from Min Rotation Angle to Max Rotation Angle.

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.

Instruction: This parameter is used to set the weight value for each point in the weight file, and there is no unit. The higher the value is, the better the matching result of the model fragments tend to be. Assuming that the weight coefficient of all initial point clouds 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.