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DNCFind (CVDNCFind.dll) 14.1
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DNCFind (CVDNCFind.dll) 14.1
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Definition of search parameters. More...
Data Fields | |
| int | DerivativePatchSize |
| Smoothing area in pixels for gradient and normal calculation. More... | |
| double | HypothesesThreshold |
| Minimum feature score for hypotheses generation. More... | |
| int | ICPMaxIterations |
| Maximum number of ICP-iterations. More... | |
| int | ICPShrink |
| Subsample factor for ICP. More... | |
| double | IndifferentRadius |
| Fraction of template size which accounts for a single object. More... | |
| double | MaxInconsistency |
| Maximum allowed fraction of point cloud view to be inconsistent with model. More... | |
| double | MaxOcclusion |
| Maximum allowed fraction of point cloud view to be occluded. More... | |
| double | MinCoverage |
| Minimum required fraction of point cloud view. More... | |
| double | MinScore |
| Minimum required final score. More... | |
| int | PartsToFind |
| Maximum number of objects to find. More... | |
| double | PrecisionThreshold |
| Maximum allowed distance for local deviations (in mm). More... | |
| bool | RawResultsOnly |
| Take hypotheses as hits. More... | |
Definition of search parameters.
Search parameters strongly influence the search results. Likewise, they have a great influence on the timing of a search operation CVDNCFind.
| DerivativePatchSize |
Smoothing area in pixels for gradient and normal calculation.
The value controls which local environment in the depth image is used to calculate gradients and normals. Minimum is 3. Larger values result in a smoothing of the depth image. The value should be odd. Typical values are in the range 3..9.
| HypothesesThreshold |
Minimum feature score for hypotheses generation.
The value controls which areas of the depth image are used as candidates for closer examination. The feature score is calculated from the correspondences of gradients and normals between all models and the actual point cloud data. The threshold should be chosen so that on the one hand all object candidates are found, on the other hand as few false candidates as possible are generated. Typical values are in the range between 0.9 ... 1.0. Disturbances in the point cloud, missing or spurious data may make it necessary to reduce the value. The minimum value is 0.5.
To find a usable threshold it is recommended to set the flag RawResultsOnly.
| ICPMaxIterations |
Maximum number of ICP-iterations.
The value specifies the maximum number of iterations of the ICP algorithm. Increasing the value may increase the accuracy of the result, while possibly increasing the processing time. A typical value is 10.
| ICPShrink |
Subsample factor for ICP.
The value specifies the factor by which the area of a found candidate is reduced before the exact position of the object is determined by means of an ICP algorithm. The minimum allowed value of 1 means no reduction (highest accuracy). With increasing reduction, the processing speed increases, but at the same time the accuracy of the results also decrease. Typical values are in a range 1..4.
| IndifferentRadius |
Fraction of template size which accounts for a single object.
The value specifies within which vicinity of a found candidate no further candidates are searched for. The value 1 indicates the largest extent of the learned object. For elongated objects that are close to each other, a smaller value may have to be selected. The minimum value is 0.5.
| MaxInconsistency |
Maximum allowed fraction of point cloud view to be inconsistent with model.
The value is a threshold (0..1) that specifies the maximum allowed part of the model view to be insconsistend with the point cloud data in order for the hit to be counted. Inconsistency is defined to be point cloud data which is beyond the model. A typical value may be 0.2. It is influenced by PrecisionThreshold.
| MaxOcclusion |
Maximum allowed fraction of point cloud view to be occluded.
The value is a threshold (0..1) that specifies the maximum allowed part of the model view which is occluded by the point cloud data in order for the hit to be counted. Occlusion is defined to be point cloud data lying between the model and the sensor. A typical value may be 0.2. It is influenced by PrecisionThreshold.
| MinCoverage |
Minimum required fraction of point cloud view.
The value is a threshold (0..1) that specifies the minimum required coverage of the model view by the point cloud data in order for the hit to be counted. A typical value may be 0.8. It is influenced by PrecisionThreshold.
| MinScore |
Minimum required final score.
The value is a threshold (0..1) that determines whether the candidate is counted as a hit. For this, a hash similarity score between final model view and point cloud data must exceed this limit. A typical value may be 0.8.
| PartsToFind |
Maximum number of objects to find.
The value specifies the maximum number of objects to be detected. A value of zero means that all objects should be found.
| PrecisionThreshold |
Maximum allowed distance for local deviations (in mm).
The calculation of the result score is based on deviations between the CAD model and the point cloud data. This value determines which deviation is considered tolerable, inconsistent or occlusion. The value depends on the quality of the point cloud data. A typical value is 2 mm, the minimum allowed value is 0.
| RawResultsOnly |
Take hypotheses as hits.
If this flag is set, candidate locations are considered hits without further investigation of these candidates. In this case, only parameters HypothesesThreshold, PartsToFind and MinScore are decisive for finding objects. If found candidates are indeed true object hits, the result values for CVDNCResult::Position, CVDNCResult::RotationVector and CVDNCResult::Theta are only rough in a sense, that no fine tuning (ICP) takes place.
Also, in this case the reported CVDNCResult::Score values coincide with the feature scores, which makes it possible to determine a useful value for HypothesesThreshold.