meshroom.nodes.aliceVision.ImageMatching.ImageMatching
Category: Sparse Reconstruction
The goal of this node is to select the image pairs to match. The ambition is to find the images that are looking to the same areas of the scene. Thanks to this node, the FeatureMatching node will only compute the matches between the selected image pairs.
It provides multiple methods:
VocabularyTree It uses image retrieval techniques to find images that share some content without the cost of resolving all feature matches in details. Each image is represented in a compact image descriptor which allows to compute the distance between all images descriptors very efficiently. If your scene contains less than “Voc Tree: Minimal Number of Images”, all image pairs will be selected.
Sequential If your input is a video sequence, you can use this option to link images between them over time.
SequentialAndVocabularyTree Combines sequential approach with Voc Tree to enable connections between keyframes at different times.
Exhaustive Export all image pairs.
Frustum If images have known poses, computes the intersection between cameras frustums to create the list of image pairs.
FrustumOrVocabularyTree If images have known poses, use frustum intersection else use VocabularuTree.
Online
Inputs:
input (File)
featuresFolders (ListAttribute)
method (ChoiceParam)
tree (File)
weights (File)
minNbImages (IntParam)
maxDescriptors (IntParam)
nbMatches (IntParam)
nbNeighbors (IntParam)
verboseLevel (ChoiceParam)
Outputs:
output (File)
- class meshroom.nodes.aliceVision.ImageMatching.ImageMatching
- __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