meshroom.nodes.aliceVision.FeatureExtraction.FeatureExtraction
Category: Sparse Reconstruction
This node extracts distinctive groups of pixels that are, to some extent, invariant to changing camera viewpoints during image acquisition. Hence, a feature in the scene should have similar feature descriptions in all images.
This node implements multiple methods:
SIFT The most standard method. This is the default and recommended value for all use cases.
AKAZE AKAZE can be interesting solution to extract features in challenging condition. It could be able to match wider angle than SIFT but has drawbacks. It may extract to many features, the repartition is not always good. It is known to be good on challenging surfaces such as skin.
CCTAG CCTag is a marker type with 3 or 4 crowns. You can put markers in the scene during the shooting session to automatically re-orient and re-scale the scene to a known size. It is robust to motion-blur, depth-of-field, occlusion. Be careful to have enough white margin around your CCTags.
Online
https://alicevision.org/#photogrammetry/natural_feature_extraction
Inputs:
input (File)
masksFolder (File)
describerTypes (ChoiceParam)
describerPreset (ChoiceParam)
maxNbFeatures (IntParam)
describerQuality (ChoiceParam)
contrastFiltering (ChoiceParam)
relativePeakThreshold (FloatParam)
gridFiltering (BoolParam)
workingColorSpace (ChoiceParam)
forceCpuExtraction (BoolParam)
maxThreads (IntParam)
verboseLevel (ChoiceParam)
Outputs:
output (File)
- class meshroom.nodes.aliceVision.FeatureExtraction.FeatureExtraction
- __init__()
Methods
__init__()buildCommandLine(chunk)postUpdate(node)Method call after node's internal update on invalidation.
processChunk(chunk)stopProcess(chunk)update(node)Method call before node's internal update on invalidation.
upgradeAttributeValues(attrValues, fromVersion)Attributes
categorycgroupParsedcmdCorecmdMemcommandLinecommandLineRangecpudocumentationgpuinputsinternalFolderinternalInputsoutputspackageNamepackageVersionparallelizationramsize