Complete Node List

Note: Some parameters are exposed for development purposes.

Nodes/features marked with an # are not supported/implemented in the current release

* in default pipeline **tested and working ? not tested

CameraCalibration (#)

Description

Note: This node requires AliceVision compiled with opencv. Not included in the MR 2019.1 binary.

The internal camera parameters can be calibrated from multiple views of a checkerboard. This allows to retrieve focal length, principal point and distortion parameters. A detailed explanation is presented in [opencvCameraCalibration].

[opencvCameraCalibration] http://docs.opencv.org/3.0-beta/doc/tutorials/calib3d/camera_calibration/camera_calibration.html

settings

Name	Description
Input	Input images in one of the following form: – folder containing images – image sequence like “/path/to/seq.@.jpg” – video file
Pattern	Type of pattern (camera calibration patterns) - CHESSBOARD - CIRCLES - ASYMMETRIC_CIRCLES - ASYMMETRIC_CCTAG
Size	(Size of the Pattern) - Number of inner corners per one of board dimension like Width Height
Square Size	Size of the grid’s square cells (0-100mm)
Nb Distortion Coef	Number of distortion coefficient (0-5)
Max Frames	Maximal number of frames to extract from the video file (0-5)
Calib Grid Size	Define the number of cells per edge (0-50)
Max Calib Frames	Maximal number of frames to use to calibrate from the selected frames (0-1000)
Min Input Frames	Minimal number of frames to limit the refinement loop (0-100)
Max Total Average Error	Max Total Average Error (0-1)
Debug Rejected Img Folder	Folder to export delete images during the refinement loop
Debug Selected Img Folder	Folder to export debug images
Output	Output filename for intrinsic [and extrinsic] parameters (default filename cameraCalibration.cal)

CameraInit

Description

-load image metadata and sensor information You can mix multiple cameras and focal lengths. The CameraInit will create groups of intrinsics based on the images metadata. It is still good to have multiple images with the same camera and same focal lengths as it adds constraints on the internal cameras parameters. But you can combine multiple groups of images, it will not decrease the quality of the final model.1

Note: In some cases, some image(s) have no serial number to identify the camera/lens device. This makes it impossible to correctly group the images by device if you have used multiple identical (same model) camera devices. The reconstruction will assume that only one device has been used, so if 2 images share the same focal length approximation they will share the same internal camera parameters. If you want to use multiple cameras, add a corresponding serialnumber to the EXIF data.

settings

Name	Description
Viewpoints Input viewpoints	(1 Element for each loaded image) ID Pose ID Image Path Intrinsic: Internal Camera Parameters (Intrinsic ID) Rig (-1 - 200) Rig Sub-Pose: Rig Sub-Pose Parameters (-1 - 200) Image Metadata: (list of metadata elements)
Intrinsic Camera Intrinsics	(1 Element for each loaded image) ID Initial Focal Length: Initial Guess on the Focal Length Focal Length: Known/Calibrated Focal Length Camera Type: pinhole’, ‘radial1’, ‘radial3’, ‘brown’, ‘fisheye4’ #Make: Camera Make (not included in this build, commented out) #Model: Camera Model #Sensor Width: Camera Sensor Width Width: Image Width (0-10000) Height: Image Height (0-10000) Serial Number: Device Serial Number (camera and lens combined) Principal Point: X (0-10000) Y(0-10000) DistortionParams: Distortion Parameters Locked(True/False): If the camera has been calibrated, the internal camera parameters (intrinsics) can be locked. It should improve robustness and speedup the reconstruction.
Sensor Database	Camera sensor width database path
Default Field Of View	Empirical value for the field of view in degree 45° (0°-180°)
Verbose Level	verbosity level (fatal, error, warning, info, debug, trace)
Output SfMData File	…/cameraInit.sfm

Notes

Issue: structure from motion reconstruction appears distorted, and has failed to aligned some groups of cameras when loading images without focallength

Solution: Keep the ” Focal Length” init value but set the “Initial Focal Length” to -1 if you are not sure of the value.

https://github.com/alicevision/meshroom/issues/434

CameraLocalization (?)

Description

Based on the SfM results, we can perform camera localization and retrieve the motion of an animated camera in the scene of the 3D reconstruction. This is very useful for doing texture reprojection in other software as part of a texture clean up pipeline. Could also be used to leverage Meshroom as a 3D camera tracker as part of a VFX pipeline

https://alicevision.github.io/#photogrammetry/localization

settings

Name	Description
SfM Data	The sfm_data.json kind of file generated by AliceVision
Media File	The folder path or the filename for the media to track
Visual Debug Folder	If a folder is provided it enables visual debug and saves all the debugging info in that folder
Descriptor Path	Folder containing the descriptors for all the images (ie the .desc.)
Match Desc Types	Describer types to use for the matching: sift’, ‘sift_float’, ‘sift_upright’, ‘akaze’, ‘akaze_liop’, ‘akaze_mldb’, ‘cctag3’, ‘cctag4’, ‘sift_ocv’, ‘akaze_ocv
Preset	Preset for the feature extractor when localizing a new image (low, medium, normal, high, ultra)
Resection Estimator	The type of /sac framework to use for resection (acransac, loransac)
Matching Estimator	The type of /sac framework to use for matching (acransac, loransac)
Calibration	Calibration file
Refine Intrinsics	Enable/Disable camera intrinsics refinement for each localized image
Reprojection Error	Maximum reprojection error (in pixels) allowed for resectioning. If set to 0 it lets the ACRansac select an optimal value (0.1 - 50)
Nb Image Match	[voctree] Number of images to retrieve in database (1 - 1000)
Max Results	[voctree] For algorithm AllResults, it stops the image matching when this number of matched images is reached. If 0 it is ignored (1 - 100)
Commonviews	[voctree] Number of minimum images in which a point must be seen to be used in cluster tracking (2 - 50)
Voctree	[voctree] Filename for the vocabulary tree
Voctree Weights	[voctree] Filename for the vocabulary tree weights
Algorithm	[voctree] Algorithm type: (FirstBest, AllResults)
Matching Error	[voctree] Maximum matching error (in pixels) allowed for image matching with geometric verification. If set to 0 it lets the ACRansac select an optimal value (0 - 50)
Nb Frame Buffer Matching	[voctree] Number of previous frame of the sequence to use for matching (0 = Disable) (0 - 100)
Robust Matching	[voctree] Enable/Disable the robust matching between query and database images, all putative matches will be considered
N Nearest Key Frames	[cctag] Number of images to retrieve in the database Parameters specific for final (optional) bundle adjustment optimization of the sequence: (1-100)
Global Bundle	[bundle adjustment] If –refineIntrinsics is not set, this option allows to run a final global bundle adjustment to refine the scene
No Distortion	[bundle adjustment] It does not take into account distortion during the BA, it consider the distortion coefficients all equal to 0
No BA Refine Intrinsics	[bundle adjustment] It does not refine intrinsics during BA
Min Point Visibility	[bundle adjustment] Minimum number of observation that a point must have in order to be considered for bundle adjustment (2-50)
Output Alembic	Filename for the SfMData export file (where camera poses will be stored) desc.Node.internalFolder + ‘trackedCameras.abc
Output JSON	Filename for the localization results as .json desc.Node.internalFolder + ‘trackedCameras.json

CameraRigCalibration (?)

Description

If a rig of cameras is used, we can perform the rig calibration. We localize cameras individually on the whole sequence. Then we use all valid poses to compute the relative poses between cameras of the rig and choose the more stable value across the images. Then we initialize the rig relative pose with this value and perform a global Bundle Adjustment on all the cameras of the rig. When the rig is calibrated, we can use it to directly localize the rig pose from the synchronized multi-cameras system with [Kneip2014] approaches.

..The rig calibration find the relative poses between all cameras used. It takes a point cloud as input and can use both CCTag and SIFT features for localization. The implication is that all cameras must see features (either SIFT or CCTag) that are part of the point cloud, but they do not have to observe overlapping regions. (See:POPART: Previz for Onset Production Adaptive Realtime Tracking)

“Given the position of the tracked reference frame relative to the motion capture system and the optical reference frames it is possible to retrieve the transformation between the tracked and the optical reference frames”1 “In practice, it is particularly difficult to make the tracked frame coincident with the camera optical frame, thus a calibration procedure is needed to estimate this transformation and achieve the millimetric accuracy” [Chiodini et al. 2018]

[Chiodini et al. 2018] Chiodini, Sebastiano & Pertile, Marco & Giubilato, Riccardo & Salvioli, Federico & Barrera, Marco & Franceschetti, Paola & Debei, Stefano. (2018). Camera Rig Extrinsic Calibration Using a Motion Capture System. 10.1109/MetroAeroSpace.2018.8453603. https://www.researchgate.net/publication/327513182_Camera_Rig_Extrinsic_Calibration_Using_a_Motion_Capture_System

https://alicevision.github.io/#photogrammetry/localization

[Kneip2011] A Novel Parametrization of the Perspective-Three-Point Problem for a Direct Computation of Absolute Camera Position and Orientation. L. Kneip, D. Scaramuzza, R. Siegwart. June 2011

[Kneip2013] Using Multi-Camera Systems in Robotics: Efficient Solutions to the NPnP ProblemL. Kneip, P. Furgale, R. Siegwart. May 2013

[Kneip2014] OpenGV: A unified and generalized approach to real-time calibrated geometric vision, L. Kneip, P. Furgale. May 2014.

[Kneip2014] Efficient Computation of Relative Pose for Multi-Camera Systems. L. Kneip, H. Li. June 2014

settings

Name	Description
SfM Data	The sfmData file
Media Path	The path to the video file	the folder of the image sequence or a text file (one image path per line) for each camera of the rig (eg. –mediapath /path/to/cam1.mov /path/to/cam2.mov)
Camera Intrinsics	The intrinsics calibration file for each camera of the rig. (eg. –cameraIntrinsics /path/to/calib1.txt /path/to/calib2.txt)
Export	Filename for the alembic file containing the rig poses with the 3D points. It also saves a file for each camera named ‘filename.cam##.abc (trackedcameras.abc)
Descriptor Path	Folder containing the .desc
Match Describer Types	The describer types to use for the matching ``’sift’	‘sift_float’	‘sift_upright’	‘akaze’	‘akaze_liop’	‘akaze_mldb’	‘cctag3’	‘cctag4’	‘sift_ocv’	‘akaze_ocv’``
Preset	Preset for the feature extractor when localizing a new image (low	medium	normal	high	ultra)
Resection Estimator	The type of /sac framework to use for resection (acransac	loransac)
Matching Estimator	The type of /sac framework to use for matching (acransac	loransac)
Refine Intrinsics	Enable/Disable camera intrinsics refinement for each localized image
Reprojection Error	Maximum reprojection error (in pixels) allowed for resectioning. If set to 0 it lets the ACRansac select an optimal value. (0 - 10)
Max Input Frames	Maximum number of frames to read in input. 0 means no limit (0 - 1000)
Voctree	[voctree] Filename for the vocabulary tree
Voctree Weights	[voctree] Filename for the vocabulary tree weights
Algorithm	[voctree] Algorithm type: {FirstBest	AllResults}
Nb Image Match	[voctree] Number of images to retrieve in the database (0 - 50)
Max Results	[voctree] For algorithm AllResults	it stops the image matching when this number of matched images is reached. If 0 it is ignored (0 - 100)
Matching Error	[voctree] Maximum matching error (in pixels) allowed for image matching with geometric verification. If set to 0 it lets the ACRansac select an optimal value (0 - 10)
N Nearest Key Frames	[cctag] Number of images to retrieve in database (0 - 50)
Output File	The name of the file where to store the calibration data (desc.Node.internalFolder + ‘cameraRigCalibration.rigCal)

Voctree Weights: http://www.ipol.im/pub/art/2018/199/ voctree (optional): For larger datasets (>200 images), greatly improves image matching performances. It can be downloaded here. https://github.com/fragofer/voctree You need to specify the path to vlfeat_K80L3.SIFT.tree in Voctree.

CameraRigLocalization (?)

Description

This node retrieves the transformation between the tracked and the optical reference frames.(?) https://alicevision.github.io/#photogrammetry/localization

settings

Name	Description
SfM Data	The sfmData file
Media Path	The path to the video file	the folder of the image sequence or a text file (one image path per line) for each camera of the rig (eg. –mediapath /path/to/cam1.mov /path/to/cam2.mov)
Rig Calibration File	The file containing the calibration data for the rig (subposes)
Camera Intrinsics	The intrinsics calibration file for each camera of the rig. (eg. –cameraIntrinsics /path/to/calib1.txt /path/to/calib2.txt)
Descriptor Path	Folder containing the .desc
Match Describer Types	The describer types to use for the matching ``(sift’	‘sift_float’	‘sift_upright’	‘akaze’	‘akaze_liop’	‘akaze_mldb’	‘cctag3’	‘cctag4’	‘sift_ocv’	‘akaze_ocv’)``
Preset	Preset for the feature extractor when localizing a new image ``(low	medium	normal	high	ultra)``
Resection Estimator	The type of /sac framework to use for resection ``(acransac	loransac)``
Matching Estimator	The type of /sac framework to use for matching ``(acransac	loransac)``
Refine Intrinsics	Enable/Disable camera intrinsics refinement for each localized image
Reprojection Error	Maximum reprojection error (in pixels) allowed for resectioning. If set to 0 it lets the ACRansac select an optimal value (0 - 10)
Use Localize Rig Naive	Enable/Disable the naive method for rig localization: naive method tries to localize each camera separately
Angular Threshold	The maximum angular threshold in degrees between feature bearing vector and 3D point direction. Used only with the opengv method (0 - 10)
Voctree	[voctree] Filename for the vocabulary tree
Voctree Weights	[voctree] Filename for the vocabulary tree weights
Algorithm	[voctree] Algorithm type: ``{FirstBest	AllResults}``
Nb Image Match	[voctree] Number of images to retrieve in the database
Max Results	[voctree] For algorithm AllResults	it stops the image matching when this number of matched images is reached. If 0 it is ignored (0 - 100)
Matching Error	[voctree] Maximum matching error (in pixels) allowed for image matching with geometric verification. If set to 0 it lets the ACRansac select an optimal value (0 - 10)
N Nearest Key Frames	[cctag] Number of images to retrieve in database (0 - 50)
Output Alembic	Filename for the SfMData export file (where camera poses will be stored) desc.Node.internalFolder + ‘trackedcameras.abc

ConvertSfMFormat

Description

• creates abc’, ‘sfm’, ‘json’, ‘ply’, ‘baf SfM File from SfMData file

settings

Name	Description
Input	SfMData file
SfM File Format	SfM File Format ``(output file extension: abc’	‘sfm’	‘json’	‘ply’	‘baf)``
Describer Types	Describer types to keep.``’sift’	‘sift_float’	‘sift_upright’	‘akaze’	‘akaze_liop’	‘akaze_mldb’	‘cctag3’	‘cctag4’	‘sift_ocv’	‘akaze_ocv’``
Image id	Image id
Image White List	image white list (uids or image paths).
Views	Export views
Intrinsics	Export intrinsics
Extrinsics	Export extrinsics
Structure	Export structure
Observations	Export observations
Verbose Level	verbosity level ``(fatal	error	warning	info	debug	trace)``
Output	Path to the output SfM Data file. (desc.Node.internalFolder + ‘sfm.{fileExtension})

Input nodes: StructureFromMotion:output->input:ConvertSfMFormat

Can I convert between Openmvg and alicevision SfM formats?

OpenMVG and AliceVision json formats are very similar in the structure but not compatible right away as openmvg is a data serialization file among other things. https://github.com/alicevision/AliceVision/issues/600

DepthMap

Description

---

settings

Name	Description
MVS Configuration File:	SfMData file.
Images Folder	Use images from a specific folder instead of those specify in the SfMData file.Filename should be the image uid.
Downscale	Image downscale factor ``(1	2	4	8	16)``
Min View Angle	Minimum angle between two views. ``(0.0	10.0	0.1)``
Max View Angle	Maximum angle between two views. ``(10.0	120.0	1)``
SGM: Nb Neighbour Cameras	Semi Global Matching: Number of neighbour cameras (1 - 100)
SGM: WSH: Semi Global Matching	Half-size of the patch used to compute the similarity (1 - 20)
SGM: GammaC	Semi Global Matching: GammaC Threshold (0 - 30)
SGM: GammaP	Semi Global Matching: GammaP Threshold (0 - 30)
Refine: Number of samples	(1 - 500)
Refine: Number of Depths	(1 - 100)
Refine: Number of Iterations	(1 - 500)
Refine: Nb Neighbour Cameras	Refine: Number of neighbour cameras. (1 - 20)
Refine: WSH	Refine: Half-size of the patch used to compute the similarity. (1 - 20)
Refine: Sigma	Refine: Sigma Threshold (0 - 30)
Refine: GammaC	Refine: GammaC Threshold. (0 - 30)
Refine: GammaP	Refine: GammaP threshold. (0 - 30)
Refine: Tc or Rc pixel size	Use minimum pixel size of neighbour cameras (Tc) or current camera pixel size (Rc)
Verbose Level	verbosity level (fatal	error	warning	info	debug	trace)
Output	Output folder for generated depth maps

default:

DepthMapFilter

Description

The original depth maps will not be entirely consistent. Certain depth maps will claim to see areas that are occluded by other depth maps. The DepthMapFilter step isolates these areas and forces depth consistency.

settings

Name	Description
Input	SfMData file
Depth Map Folder	Input depth map folder
Number of Nearest Cameras	Number of nearest cameras used for filtering 10 (0 - 20)
Min Consistent Cameras	Min Number of Consistent Cameras 3 (0 - 10)
Min Consistent Cameras Bad Similarity	Min Number of Consistent Cameras for pixels with weak similarity value 4 (0 - 10)
Filtering Size in Pixels	Filtering size in Pixels (0 - 10)
Filtering Size in Pixels Bad Similarity	Filtering size in pixels (0 - 10)
Verbose Level	verbosity level (fatal	error	warning	info	debug	trace)
Output	Output folder for generated depth maps

Min Consistent Cameras lower this value if the Meshing node has 0 depth samples input

View Output open output folder and view EXR files

ExportAnimatedCamera

Description

creates an Alembic animatedCamera.abc file from SFMData (e.g. for use in 3D Compositing software)

settings

Name	Description
Input SfMData	SfMData file containing a complete SfM
SfMData Filter	A SfMData file use as filter
Export Undistorted Images	Export Undistorted Images value=True
Undistort Image Format	Image file format to use for undistorted images ``(*.jpg	*.jpg	*.tif	*.exr (half))``
Verbose Level	Verbosity level ``(fatal	error	warning	info	debug	trace)``
Output filepath	Output filepath for the alembic animated camera
Output Camera Filepath	Output filename for the alembic animated camera internalFolder + ‘camera.abc’

SFM->ExportAnimatedCamera .. details https://www.youtube.com/watch?v=1dhdEmGLZhY

ExportMaya

Description

Mode for use with MeshroomMaya plugin.

The node “ExportMaya” exports the undistorted images. This node has nothing dedicated to Maya but was used to import the data into our MeshroomMaya plugin. You can use the same to export to Blender.

settings

Name	Description
Input SfM Data	sfm.sfm or sfm.abc
Output Folder	Folder for MeshroomMaya output: undistorted images and thumbnails

ExportMaya: requires .sfm or .abc as input from ConvertSfMFormat

FeatureExtraction

Description

---

---

FeatureMatching

Description

---

settings

Name	Description
Input	SfMData file
Features Folder
Features Folders	Folder(s) containing the extracted features and descriptors
Image Pairs List	Path to a file which contains the list of image pairs to match
Describer Types	Describer types used to describe an image **sift**'/ 'sift_float'/ 'sift_upright'/ 'akaze'/ 'akaze_liop'/ 'akaze_mldb'/ 'cctag3'/ 'cctag4'/ 'sift_ocv'/ 'akaze_ocv
Photometric Matching Method	For Scalar based regions descriptor ' * BRUTE_FORCE_L2: L2 BruteForce matching' ' * ANN_L2: L2 Approximate Nearest Neighbor matching ' * CASCADE_HASHING_L2: L2 Cascade Hashing matching ' * FAST_CASCADE_HASHING_L2: L2 Cascade Hashing with precomputed hashed regions (faster than CASCADE_HASHING_L2 but use more memory) 'For Binary based descriptor  ' * BRUTE_FORCE_HAMMING: BruteForce Hamming matching'
Geometric Estimator	Geometric estimator: (acransac:  A-Contrario Ransac //  loransac: LO-Ransac (only available for fundamental_matrix model)
Geometric Filter Type	Geometric validation method to filter features matches: **fundamental_matrix** // essential_matrix // homography_matrix /// homography_growing // no_filtering'
Distance Ratio	Distance ratio to discard non meaningful matches 0.8 (0.0 - 1)
Max Iteration	Maximum number of iterations allowed in ransac step 2048 (1 - 20000)
Max Matches	Maximum number of matches to keep (0 - 10000)
Save Putative Matches	putative matches (True/False)
Guided Matching	the found model to improve the pairwise correspondences (True/False)
Export Debug Files	debug files (svg/ dot) (True/False)
Verbose Level	verbosity level (fatal/ error	warning	info	debug	trace)
Output Folder	Path to a folder in which computed matches will be stored

ImageMatching

Description

---

settings

Name	Description
Image	SfMData file
Features Folders	Folder(s) containing the extracted features and descriptors
Tree	Input name for the vocabulary tree file ALICEVISION_VOCTREE
Weights	Input name for the weight file	if not provided the weights will be computed on the database built with the provided set
Minimal Number of Images	Minimal number of images to use the vocabulary tree. If we have less features than this threshold	we will compute all matching combinations
Max Descriptors	Limit the number of descriptors you load per image. Zero means no limit
Nb Matches	The number of matches to retrieve for each image (If 0 it will retrieve all the matches) 50 (0-1000)
Verbose Level	verbosity level (fatal	error	warning	info	debug	trace)
Output List File	Filepath to the output file with the list of selected image pairs

ImageMatchingMultiSfM

Description

This node can combine image matching between two input SfMData.

Used for Live Reconstructin and Augmentation

settings

Name	Description
Input A	SfMData file
Input B	SfMData file
Features Folders	Folder(s) containing the extracted features and descriptors
Tree	Input name for the vocabulary tree file ALICEVISION_VOCTREE
Weights	Input name for the weight file if not provided the weights will be computed on the database built with the provided set
Matching Mode	The mode to combine image matching between the input SfMData A and B: a/a+a/b for A with A + A with B. a/ab [‘a/a+a/b’ // ‘a/ab’ // ‘a/b’]
Minimal Number of Images	Minimal number of images to use the vocabulary tree. If we have less features than this threshold we will compute all matching combinations
Max Descriptors	Limit the number of descriptors you load per image. Zero means no limit 500 (0-100000)
Nb Matches	The number of matches to retrieve for each image (If 0 it will retrieve all the matches) 50 (0-1000)
Verbose Level	verbosity level (fatal // error // warning // info // debug // trace)
Output List File	Filepath to the output file with the list of selected image pairs
Output Combined SfM	Path for the combined SfMData file internalFolder + ‘combineSfM.sfm

KeyframeSelection

Description Note: This is an experimental node for keyframe selection in a video, which removes too similar or too blurry images. This node is not yet provided in the binaries as it introduces many dependencies. So if you built it by yourself, you can test the KeyframeSelection node. It is not yet fully integrated into Meshroom, so you have to manually drag&drop the exported frames to launch the reconstruction (instead of just adding a connection in the graph) https://github.com/alicevision/meshroom/issues/232

---

MeshDecimate

Description

Simplify your mesh to reduce mesh size without changing visual appearance of the model.

settings

Name	Description
Input Mesh (OBJ file format)
Simplification factor	Simplification factor 0.5 (0 - 1)
Fixed Number of Vertice	Fixed number of output vertices 0 (0 - 1 000 000)
Min Vertices	Min number of output vertices 0 (0 - 1 000 000)
Max Vertices	Max number of output vertices 0 (0 - 1 000 000)
Flip Normals	Option to flip face normals ‘It can be needed as it depends on the vertices order in triangles and the convention change from one software to another. (True/False)
Verbose Level	verbosity level (fatal // error // warning // info // debug // trace)
Output mesh	Output mesh (OBJ file format) internalFolder + ‘mesh.obj

or Meshing->MeshDecimate->MeshFiltering?

Comparison MeshDecimate and MeshResampling

Flip Normals

MeshDenoising

Description

Denoise your mesh Mesh models generated by 3D scanner always contain noise. It is necessary to remove the noise from the meshes. Mesh denoising: remove noises, feature-preserving https://www.cs.cf.ac.uk/meshfiltering/index_files/Doc/Random%20Walks%20for%20Mesh%20Denoising.ppt

settings

Name	Description
input	Input Mesh (OBJ file format)
Denoising Iterations	Number of denoising iterations ``(0	30	5``
Mesh Update Closeness Weight	``Closeness weight for mesh update	must be positive ``(0.0	0.1	0.001) 0.001``
Lambda	Regularization weight. (0.0 // 10.0 // 0.01) 2
Eta	``Gaussian standard deviation for spatial weight	scaled by the average distance between adjacent face centroids. Must be positive.(0.0	20.0	0.01) 1.5``
Mu	``Gaussian standard deviation for guidance weight (0.0	10.0	0.01) 1.5``
Nu	``Gaussian standard deviation for signal weight. (0.0	5.0	0.01) 0.3``
Mesh Update Method	``Mesh Update Method * ITERATIVE_UPDATE (default): ShapeUp styled iterative solver * POISSON_UPDATE: Poisson-based update from [Want et al. 2015] (0	1)``
Verbose Level	``[‘fatal’	‘error’	‘warning’	‘info’	‘debug’	‘trace’]``
Output	Output mesh (OBJ file format).

Mesh Update Method https://www.researchgate.net/publication/275104101_Poisson-driven_seamless_completion_of_triangular_meshes

MeshFiltering

Description

Filter out unwanted elements of your mesh

settings

Name	Description
Input	Input Mesh (OBJ file format)
Filter Large Triangles Factor	Remove all large triangles. We consider a triangle as large if one edge is bigger than N times the average edge length. Put zero to disable it. 60 (1 - 100)
Keep Only the Largest Mesh	Keep only the largest connected triangles group (True/False)
Nb Iterations	5 (0 - 50)
Lambda	1 (0-10
Verbose Level
Verbose Level	``[‘fatal’	‘error’	‘warning’	‘info’	‘debug’	‘trace’]``
Output mesh	Output mesh (OBJ file format) internalFolder + ‘mesh.obj

Note: “Keep Only The Largest Mesh”. This is disabled by default in the 2019.1.0 release to avoid that the environment is being meshed, but not the object of interest. The largest Mesh is in some cases the reconstructed background. When the object of interest is not connected to the large background mesh it will be removed. You should place your object of interest on a well structured non transparent or reflecting surface (e.g. a newspaper).

MeshResampling

Description

Reducing number of faces while trying to keep overall shape, volume and boundaries You can specify a fixed, min, max Vertices number.

This is different from MeshDecimate!

Resampling https://users.cg.tuwien.ac.at/stef/seminar/MeshResamplingMerge1901.pdf

settings

Name	Description
Input	Input Mesh (OBJ file format)
Simplification factor	Simplification factor 0.5 (0 - 1)
Fixed Number of Vertice	Fixed number of output vertices 0 (0 - 1 000 000)
Min Vertices	Min number of output vertices 0 (0 - 1 000 000)
Max Vertices	Max number of output vertices 0 (0 - 1 000 000)
Number of Pre-Smoothing Iteration	Number of iterations for Lloyd pre-smoothing 40 (0 - 100)
Flip Normals	``Option to flip face normals	It can be needed as it depends on the vertices order in triangles and the convention change from one software to another. (True/False)``
Verbose Level	``[‘fatal’	‘error’	‘warning’	‘info’	‘debug’	‘trace’]``
Output mesh	Output mesh (OBJ file format) internalFolder + mesh.obj

Comparison MeshDecimate and MeshResampling

Flip Normals

Meshing

Description

none

PrepareDenseScene

Description

• This node undistorts the images and generates EXR images

settings

Name	Description
Input	SfMData file
Verbose Level	[‘fatal’ // ‘error’ // ‘warning’ // ‘info’ // ‘debug’ // ‘trace’]
Output	MVS Configuration file (desc.Node.internalFolder + ‘mvs.ini)

Publish

Description

• A copy of the Input files are placed in the Output Folder

Can be used to save SfM, Mesh or textured Model to a specific folder

settings

Name	Description
Input Files	Input Files to publish
Output Folder	Folder to publish files to

SfMAlingnment

Description align SfM file to a scene

settings

Name	Description
Input	SfMData file
Reference	Path to the scene used as the reference coordinate system
Verbose Level	[‘fatal’ // ‘error’ // ‘warning’ // ‘info’ // ‘debug’ // ‘trace’]
Output	Aligned SfMData file internalFolder + ‘alignedSfM.abc

SfMTransform

Description

Apply a given transformation camera as the origin of the coordinate system with the SfMTransform node. You can rescale the scene based on the bounding box of CCTAG markers.

StructureFromMotion

Description

none

Texturing

Description

Texturing creates UVs and projects the textures change quality and size/ file type of texture