diff --git a/meshroom/nodes/aliceVision/CameraInit.py b/meshroom/nodes/aliceVision/CameraInit.py
index 23817b40..bc2814a6 100644
--- a/meshroom/nodes/aliceVision/CameraInit.py
+++ b/meshroom/nodes/aliceVision/CameraInit.py
@@ -16,20 +16,39 @@ Viewpoint = [
     desc.IntParam(name="intrinsicId", label="Intrinsic", description="Internal Camera Parameters", value=-1, uid=[0], range=None),
     desc.IntParam(name="rigId", label="Rig", description="Rig Parameters", value=-1, uid=[0], range=None),
     desc.IntParam(name="subPoseId", label="Rig Sub-Pose", description="Rig Sub-Pose Parameters", value=-1, uid=[0], range=None),
-    desc.StringParam(name="metadata", label="Image Metadata", description="", value="", uid=[], advanced=True),
+    desc.StringParam(name="metadata", label="Image Metadata",
+                     description="The configuration of the Viewpoints is based on the images metadata.\n"
+                                 "The important ones are:\n"
+                     " * Focal Length: the focal length in mm.\n"
+                     " * Make and Model: this information allows to convert the focal in mm into a focal length in pixel using an embedded sensor database.\n"
+                     " * Serial Number: allows to uniquely identify a device so multiple devices with the same Make, Model can be differentiated and their internal parameters are optimized separately.",
+                     value="", uid=[], advanced=True),
 ]
 
 Intrinsic = [
     desc.IntParam(name="intrinsicId", label="Id", description="Intrinsic UID", value=-1, uid=[0], range=None),
-    desc.FloatParam(name="pxInitialFocalLength", label="Initial Focal Length", description="Initial Guess on the Focal Length", value=-1.0, uid=[0], range=None),
-    desc.FloatParam(name="pxFocalLength", label="Focal Length", description="Known/Calibrated Focal Length", value=-1.0, uid=[0], range=None),
-    desc.ChoiceParam(name="type", label="Camera Type", description="Camera Type", value="", values=['', 'pinhole', 'radial1', 'radial3', 'brown', 'fisheye4', 'equidistant_r3'], exclusive=True, uid=[0]),
+    desc.FloatParam(name="pxInitialFocalLength", label="Initial Focal Length",
+                    description="Initial Guess on the Focal Length (in pixels). \n"
+                    "When we have an initial value from EXIF, this value is not accurate but cannot be wrong. \n"
+                    "So this value is used to limit the range of possible values in the optimization. \n"
+                    "If you put -1, this value will not be used and the focal length will not be bounded.",
+                    value=-1.0, uid=[0], range=None),
+    desc.FloatParam(name="pxFocalLength", label="Focal Length", description="Known/Calibrated Focal Length (in pixels)", value=-1.0, uid=[0], range=None),
+    desc.ChoiceParam(name="type", label="Camera Type",
+                     description="Mathematical Model used to represent a camera:\n"
+                     " * pinhole: Simplest projective camera model without optical distortion (focal and optical center).\n"
+                     " * radial1: Pinhole camera with one radial distortion parameter\n"
+                     " * radial3: Pinhole camera with 3 radial distortion parameters\n"
+                     " * brown: Pinhole camera with 3 radial and 2 tangential distortion parameters\n"
+                     " * fisheye4: Pinhole camera with 4 distortion parameters suited for fisheye optics (like 120° FoV)\n"
+                     " * equidistant_r3: Non-projective camera model suited for full-fisheye optics (like 180° FoV)\n",
+                     value="", values=['', 'pinhole', 'radial1', 'radial3', 'brown', 'fisheye4', 'equidistant_r3'], exclusive=True, uid=[0]),
     desc.IntParam(name="width", label="Width", description="Image Width", value=0, uid=[], range=(0, 10000, 1)),
     desc.IntParam(name="height", label="Height", description="Image Height", value=0, uid=[], range=(0, 10000, 1)),
     desc.FloatParam(name="sensorWidth", label="Sensor Width", description="Sensor Width (mm)", value=36, uid=[], range=(0, 1000, 1)),
     desc.FloatParam(name="sensorHeight", label="Sensor Height", description="Sensor Height (mm)", value=24, uid=[], range=(0, 1000, 1)),
-    desc.StringParam(name="serialNumber", label="Serial Number", description="Device Serial Number (camera and lens combined)", value="", uid=[]),
-    desc.GroupAttribute(name="principalPoint", label="Principal Point", description="", groupDesc=[
+    desc.StringParam(name="serialNumber", label="Serial Number", description="Device Serial Number (Camera UID and Lens UID combined)", value="", uid=[]),
+    desc.GroupAttribute(name="principalPoint", label="Principal Point", description="Position of the Optical Center in the Image (i.e. the sensor surface).", groupDesc=[
         desc.FloatParam(name="x", label="x", description="", value=0, uid=[], range=(0, 10000, 1)),
         desc.FloatParam(name="y", label="y", description="", value=0, uid=[], range=(0, 10000, 1)),
         ]),
@@ -96,6 +115,21 @@ class CameraInit(desc.CommandLineNode):
 
     size = desc.DynamicNodeSize('viewpoints')
 
+    documentation = '''
+This node describes your dataset. It lists the Viewpoints candidates, the guess about the type of optic, the initial focal length
+and which images are sharing the same internal camera parameters, as well as potential cameras rigs.
+
+When you import new images into Meshroom, this node is automatically configured from the analysis of the image metadata.
+The software can support images without any metadata but it is recommended to have them for robustness.
+
+### Metadata
+Metadata allows images to be grouped together and provides an initialization of the focal length (in pixel unit).
+The metadata needed are:
+ * **Focal Length**: the focal length in mm.
+ * **Make** & **Model**: this information allows to convert the focal in mm into a focal length in pixel using an embedded sensor database.
+ * **Serial Number**: allows to uniquely identify a device so multiple devices with the same Make, Model can be differentiated and their internal parameters are optimized separately (in the photogrammetry case).
+'''
+
     inputs = [
         desc.ListAttribute(
             name="viewpoints",
diff --git a/meshroom/nodes/aliceVision/ConvertSfMFormat.py b/meshroom/nodes/aliceVision/ConvertSfMFormat.py
index 5260a59b..2ffc8022 100644
--- a/meshroom/nodes/aliceVision/ConvertSfMFormat.py
+++ b/meshroom/nodes/aliceVision/ConvertSfMFormat.py
@@ -6,7 +6,12 @@ from meshroom.core import desc
 class ConvertSfMFormat(desc.CommandLineNode):
     commandLine = 'aliceVision_convertSfMFormat {allParams}'
     size = desc.DynamicNodeSize('input')
-    
+
+    documentation = '''
+Convert an SfM scene from one file format to another.
+It can also be used to remove specific parts of from an SfM scene (like filter all 3D landmarks or filter 2D observations).
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/DepthMap.py b/meshroom/nodes/aliceVision/DepthMap.py
index 24fa4300..3c459622 100644
--- a/meshroom/nodes/aliceVision/DepthMap.py
+++ b/meshroom/nodes/aliceVision/DepthMap.py
@@ -10,6 +10,16 @@ class DepthMap(desc.CommandLineNode):
     parallelization = desc.Parallelization(blockSize=3)
     commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
 
+    documentation = '''
+For each camera that have been estimated by the Structure-From-Motion, it estimates the depth value per pixel.
+
+Adjust the downscale factor to compute depth maps at a higher/lower resolution.
+Use a downscale factor of one (full-resolution) only if the quality of the input images is really high (camera on a tripod with high-quality optics).
+
+## Online
+[https://alicevision.org/#photogrammetry/depth_maps_estimation](https://alicevision.org/#photogrammetry/depth_maps_estimation)
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/DepthMapFilter.py b/meshroom/nodes/aliceVision/DepthMapFilter.py
index a4f2ed5d..7dd0eb04 100644
--- a/meshroom/nodes/aliceVision/DepthMapFilter.py
+++ b/meshroom/nodes/aliceVision/DepthMapFilter.py
@@ -10,6 +10,11 @@ class DepthMapFilter(desc.CommandLineNode):
     parallelization = desc.Parallelization(blockSize=10)
     commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
 
+    documentation = '''
+Filter depth map values that are not coherent in multiple depth maps.
+This allows to filter unstable points before starting the fusion of all depth maps in the Meshing node.
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/ExportAnimatedCamera.py b/meshroom/nodes/aliceVision/ExportAnimatedCamera.py
index b8d97557..eefeb489 100644
--- a/meshroom/nodes/aliceVision/ExportAnimatedCamera.py
+++ b/meshroom/nodes/aliceVision/ExportAnimatedCamera.py
@@ -6,6 +6,11 @@ from meshroom.core import desc
 class ExportAnimatedCamera(desc.CommandLineNode):
     commandLine = 'aliceVision_exportAnimatedCamera {allParams}'
 
+    documentation = '''
+Convert cameras from an SfM scene into an animated cameras in Alembic file format.
+Based on the input image filenames, it will recognize the input video sequence to create an animated camera.
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/ExportMaya.py b/meshroom/nodes/aliceVision/ExportMaya.py
index 41568f5d..93288526 100644
--- a/meshroom/nodes/aliceVision/ExportMaya.py
+++ b/meshroom/nodes/aliceVision/ExportMaya.py
@@ -6,6 +6,13 @@ from meshroom.core import desc
 class ExportMaya(desc.CommandLineNode):
     commandLine = 'aliceVision_exportMeshroomMaya {allParams}'
 
+    documentation = '''
+Export a scene for Autodesk Maya, with an Alembic file describing the SfM: cameras and 3D points.
+It will export half-size undistorted images to use as image planes for cameras and also export thumbnails.
+Use the MeshroomMaya plugin, to load the ABC file. It will recognize the file structure and will setup the scene.
+MeshroomMaya contains a user interface to browse all cameras.
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/FeatureExtraction.py b/meshroom/nodes/aliceVision/FeatureExtraction.py
index e5c1cfd4..a1853778 100644
--- a/meshroom/nodes/aliceVision/FeatureExtraction.py
+++ b/meshroom/nodes/aliceVision/FeatureExtraction.py
@@ -9,6 +9,26 @@ class FeatureExtraction(desc.CommandLineNode):
     parallelization = desc.Parallelization(blockSize=40)
     commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
 
+    documentation = '''
+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](https://alicevision.org/#photogrammetry/natural_feature_extraction)
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/FeatureMatching.py b/meshroom/nodes/aliceVision/FeatureMatching.py
index 0b4c00d7..f86ff7b7 100644
--- a/meshroom/nodes/aliceVision/FeatureMatching.py
+++ b/meshroom/nodes/aliceVision/FeatureMatching.py
@@ -9,6 +9,28 @@ class FeatureMatching(desc.CommandLineNode):
     parallelization = desc.Parallelization(blockSize=20)
     commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
 
+    documentation = '''
+This node performs the matching of all features between the candidate image pairs.
+
+It is performed in 2 steps:
+
+ 1/ **Photometric Matches**
+
+It performs the photometric matches between the set of features descriptors from the 2 input images.
+For each feature descriptor on the first image, it looks for the 2 closest descriptors in the second image and uses a relative threshold between them.
+This assumption kill features on repetitive structure but has proved to be a robust criterion.
+
+ 2/ **Geometric Filtering**
+
+It performs a geometric filtering of the photometric match candidates.
+It uses the features positions in the images to make a geometric filtering by using epipolar geometry in an outlier detection framework
+called RANSAC (RANdom SAmple Consensus). It randomly selects a small set of feature correspondences and compute the fundamental (or essential) matrix,
+then it checks the number of features that validates this model and iterate through the RANSAC framework.
+
+## Online
+[https://alicevision.org/#photogrammetry/feature_matching](https://alicevision.org/#photogrammetry/feature_matching)
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/GlobalSfM.py b/meshroom/nodes/aliceVision/GlobalSfM.py
index a60b7a78..42996b93 100644
--- a/meshroom/nodes/aliceVision/GlobalSfM.py
+++ b/meshroom/nodes/aliceVision/GlobalSfM.py
@@ -10,6 +10,11 @@ class GlobalSfM(desc.CommandLineNode):
     commandLine = 'aliceVision_globalSfM {allParams}'
     size = desc.DynamicNodeSize('input')
 
+    documentation = '''
+Performs the Structure-From-Motion with a global approach.
+It is known to be faster but less robust to challenging datasets than the Incremental approach.
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/ImageMatching.py b/meshroom/nodes/aliceVision/ImageMatching.py
index 3e1ffb40..5c748526 100644
--- a/meshroom/nodes/aliceVision/ImageMatching.py
+++ b/meshroom/nodes/aliceVision/ImageMatching.py
@@ -8,6 +8,28 @@ class ImageMatching(desc.CommandLineNode):
     commandLine = 'aliceVision_imageMatching {allParams}'
     size = desc.DynamicNodeSize('input')
 
+    documentation = '''
+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.
+
+## Online
+[https://alicevision.org/#photogrammetry/image_matching](https://alicevision.org/#photogrammetry/image_matching)
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/ImageMatchingMultiSfM.py b/meshroom/nodes/aliceVision/ImageMatchingMultiSfM.py
index b5601c84..2d506227 100644
--- a/meshroom/nodes/aliceVision/ImageMatchingMultiSfM.py
+++ b/meshroom/nodes/aliceVision/ImageMatchingMultiSfM.py
@@ -9,6 +9,14 @@ class ImageMatchingMultiSfM(desc.CommandLineNode):
     # use both SfM inputs to define Node's size
     size = desc.MultiDynamicNodeSize(['input', 'inputB'])
 
+    documentation = '''
+The goal of this node is to select the image pairs to match in the context of an SfM augmentation.
+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.
+
+## Online
+[https://alicevision.org/#photogrammetry/image_matching](https://alicevision.org/#photogrammetry/image_matching)
+'''
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/KeyframeSelection.py b/meshroom/nodes/aliceVision/KeyframeSelection.py
index 8d85dff8..987af423 100644
--- a/meshroom/nodes/aliceVision/KeyframeSelection.py
+++ b/meshroom/nodes/aliceVision/KeyframeSelection.py
@@ -7,6 +7,13 @@ from meshroom.core import desc
 class KeyframeSelection(desc.CommandLineNode):
     commandLine = 'aliceVision_utils_keyframeSelection {allParams}'
 
+    documentation = '''
+Allows to extract keyframes from a video and insert metadata.
+It can extract frames from a synchronized multi-cameras rig.
+
+You can extract frames at regular interval by configuring only the min/maxFrameStep.
+'''
+
     inputs = [
         desc.ListAttribute(
             elementDesc=desc.File(
diff --git a/meshroom/nodes/aliceVision/LDRToHDR.py b/meshroom/nodes/aliceVision/LDRToHDR.py
index 56e2f247..770bfcc7 100644
--- a/meshroom/nodes/aliceVision/LDRToHDR.py
+++ b/meshroom/nodes/aliceVision/LDRToHDR.py
@@ -29,6 +29,17 @@ class LDRToHDR(desc.CommandLineNode):
     cpu = desc.Level.INTENSIVE
     ram = desc.Level.NORMAL
 
+    documentation='''
+This node fuse LDR (Low Dynamic Range) images with multi-bracketing into HDR (High Dynamic Range) images.
+
+It is done in 2 steps:
+
+1/ Estimation of the Camera Response Function (CRF)
+
+2/ HDR fusion relying on the CRF
+
+'''
+
     inputs = [
         desc.File(
             name='input',
@@ -40,7 +51,7 @@ class LDRToHDR(desc.CommandLineNode):
         desc.IntParam(
             name='userNbBrackets',
             label='Number of Brackets',
-            description='Number of exposure brackets per HDR image (0 for automatic).',
+            description='Number of exposure brackets per HDR image (0 for automatic detection).',
             value=0,
             range=(0, 15, 1),
             uid=[0],
@@ -111,17 +122,18 @@ class LDRToHDR(desc.CommandLineNode):
             description="Bypass HDR creation and use the medium bracket as the source for the next steps",
             value=False,
             uid=[0],
+            advanced=True,
         ),
         desc.ChoiceParam(
             name='calibrationMethod',
             label='Calibration Method',
             description="Method used for camera calibration \n"
-                        " * linear \n"
-                        " * robertson \n"
-                        " * debevec \n"
-                        " * grossberg \n"
-                        " * laguerre",
-            values=['linear', 'robertson', 'debevec', 'grossberg', 'laguerre'],
+                        " * Linear: Disable the calibration and assumes a linear Camera Response Function. If images are encoded in a known colorspace (like sRGB for JPEG), the images will be automatically converted to linear. \n"
+                        " * Debevec: This is the standard method for HDR calibration. \n"
+                        " * Grossberg: Based on learned database of cameras, it allows to reduce the CRF to few parameters while keeping all the precision. \n"
+                        " * Laguerre: Simple but robust method estimating the minimal number of parameters. \n"
+                        " * Robertson: First method for HDR calibration in the literature. \n",
+            values=['linear', 'debevec', 'grossberg', 'laguerre', 'robertson'],
             value='debevec',
             exclusive=True,
             uid=[0],
@@ -142,7 +154,7 @@ class LDRToHDR(desc.CommandLineNode):
         desc.ChoiceParam(
             name='fusionWeight',
             label='Fusion Weight',
-            description="Weight function used to fuse all LDR images together \n"
+            description="Weight function used to fuse all LDR images together:\n"
                         " * gaussian \n"
                         " * triangle \n"
                         " * plateau",
diff --git a/meshroom/nodes/aliceVision/MeshDecimate.py b/meshroom/nodes/aliceVision/MeshDecimate.py
index 280e9319..8b928350 100644
--- a/meshroom/nodes/aliceVision/MeshDecimate.py
+++ b/meshroom/nodes/aliceVision/MeshDecimate.py
@@ -9,6 +9,10 @@ class MeshDecimate(desc.CommandLineNode):
     cpu = desc.Level.NORMAL
     ram = desc.Level.NORMAL
 
+    documentation = '''
+This node allows to reduce the density of the Mesh.
+'''
+
     inputs = [
         desc.File(
             name="input",
diff --git a/meshroom/nodes/aliceVision/MeshDenoising.py b/meshroom/nodes/aliceVision/MeshDenoising.py
index 438e611c..a7509826 100644
--- a/meshroom/nodes/aliceVision/MeshDenoising.py
+++ b/meshroom/nodes/aliceVision/MeshDenoising.py
@@ -6,6 +6,11 @@ from meshroom.core import desc
 class MeshDenoising(desc.CommandLineNode):
     commandLine = 'aliceVision_meshDenoising {allParams}'
 
+    documentation = '''
+This experimental node allows to reduce noise from a Mesh.
+for now, the parameters are difficult to control and vary a lot from one dataset to another.
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/MeshFiltering.py b/meshroom/nodes/aliceVision/MeshFiltering.py
index 4a1ae7ea..deddc28f 100644
--- a/meshroom/nodes/aliceVision/MeshFiltering.py
+++ b/meshroom/nodes/aliceVision/MeshFiltering.py
@@ -6,6 +6,11 @@ from meshroom.core import desc
 class MeshFiltering(desc.CommandLineNode):
     commandLine = 'aliceVision_meshFiltering {allParams}'
 
+    documentation = '''
+This node applies a Laplacian filtering to remove local defects from the raw Meshing cut.
+
+'''
+
     inputs = [
         desc.File(
             name='inputMesh',
diff --git a/meshroom/nodes/aliceVision/MeshResampling.py b/meshroom/nodes/aliceVision/MeshResampling.py
index e6966366..64c4cab7 100644
--- a/meshroom/nodes/aliceVision/MeshResampling.py
+++ b/meshroom/nodes/aliceVision/MeshResampling.py
@@ -9,6 +9,10 @@ class MeshResampling(desc.CommandLineNode):
     cpu = desc.Level.NORMAL
     ram = desc.Level.NORMAL
 
+    documentation = '''
+This node allows to recompute the mesh surface with a new topology and uniform density.
+'''
+
     inputs = [
         desc.File(
             name="input",
diff --git a/meshroom/nodes/aliceVision/Meshing.py b/meshroom/nodes/aliceVision/Meshing.py
index 449ef008..bef32309 100644
--- a/meshroom/nodes/aliceVision/Meshing.py
+++ b/meshroom/nodes/aliceVision/Meshing.py
@@ -9,6 +9,17 @@ class Meshing(desc.CommandLineNode):
     cpu = desc.Level.INTENSIVE
     ram = desc.Level.INTENSIVE
 
+    documentation = '''
+This node creates a dense geometric surface representation of the scene.
+
+First, it fuses all the depth maps into a global dense point cloud with an adaptive resolution.
+It then performs a 3D Delaunay tetrahedralization and a voting procedure is done to compute weights on cells and weights on facets connecting the cells.
+A Graph Cut Max-Flow is applied to optimally cut the volume. This cut represents the extracted mesh surface.
+
+## Online
+[https://alicevision.org/#photogrammetry/meshing](https://alicevision.org/#photogrammetry/meshing)
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/PanoramaCompositing.py b/meshroom/nodes/aliceVision/PanoramaCompositing.py
index 417d5fd7..eea2ae0f 100644
--- a/meshroom/nodes/aliceVision/PanoramaCompositing.py
+++ b/meshroom/nodes/aliceVision/PanoramaCompositing.py
@@ -10,6 +10,13 @@ class PanoramaCompositing(desc.CommandLineNode):
     commandLine = 'aliceVision_panoramaCompositing {allParams}'
     size = desc.DynamicNodeSize('input')
 
+    documentation = '''
+Once the images have been transformed geometrically (in PanoramaWarping),
+they have to be fused together in a single panorama image which looks like a single photography.
+The Multi-band Blending method provides the best quality. It averages the pixel values using multiple bands in the frequency domain.
+Multiple cameras are contributing to the low frequencies and only the best one contributes to the high frequencies.
+'''
+
     inputs = [
         desc.File(
             name='input',
@@ -31,7 +38,10 @@ class PanoramaCompositing(desc.CommandLineNode):
         desc.ChoiceParam(
             name='compositerType',
             label='Compositer Type',
-            description='Which compositer should be used to blend images',
+            description='Which compositer should be used to blend images:\n'
+                        ' * multiband: high quality transition by fusing images by frequency bands\n'
+                        ' * replace: debug option with straight transitions\n'
+                        ' * alpha: debug option with linear transitions\n',
             value='multiband',
             values=['replace', 'alpha', 'multiband'],
             exclusive=True,
@@ -40,7 +50,10 @@ class PanoramaCompositing(desc.CommandLineNode):
         desc.ChoiceParam(
             name='overlayType',
             label='Overlay Type',
-            description='Which overlay to display on top of panorama for debug',
+            description='Overlay on top of panorama to analyze transitions:\n'
+                        ' * none: no overlay\n'
+                        ' * borders: display image borders\n'
+                        ' * seams: display transitions between images\n',
             value='none',
             values=['none', 'borders', 'seams'],
             exclusive=True,
diff --git a/meshroom/nodes/aliceVision/PanoramaEstimation.py b/meshroom/nodes/aliceVision/PanoramaEstimation.py
index a2117daf..d58b0142 100644
--- a/meshroom/nodes/aliceVision/PanoramaEstimation.py
+++ b/meshroom/nodes/aliceVision/PanoramaEstimation.py
@@ -10,6 +10,10 @@ class PanoramaEstimation(desc.CommandLineNode):
     commandLine = 'aliceVision_panoramaEstimation {allParams}'
     size = desc.DynamicNodeSize('input')
 
+    documentation = '''
+Estimate relative camera rotations between input images.
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/PanoramaInit.py b/meshroom/nodes/aliceVision/PanoramaInit.py
index 5d3a4c07..066deec0 100644
--- a/meshroom/nodes/aliceVision/PanoramaInit.py
+++ b/meshroom/nodes/aliceVision/PanoramaInit.py
@@ -10,6 +10,18 @@ class PanoramaInit(desc.CommandLineNode):
     commandLine = 'aliceVision_panoramaInit {allParams}'
     size = desc.DynamicNodeSize('input')
 
+    documentation = '''
+This node allows to setup the Panorama:
+
+1/ Enables the initialization the cameras from known position in an XML file (provided by
+["Roundshot VR Drive"](https://www.roundshot.com/xml_1/internet/fr/application/d394/d395/f396.cfm) ).
+
+2/ Enables to setup Full Fisheye Optics (to use an Equirectangular camera model).
+
+3/ To automatically detects the Fisheye Circle (radius + center) in input images or manually adjust it.
+
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/PanoramaWarping.py b/meshroom/nodes/aliceVision/PanoramaWarping.py
index a127fe35..c44e7b7a 100644
--- a/meshroom/nodes/aliceVision/PanoramaWarping.py
+++ b/meshroom/nodes/aliceVision/PanoramaWarping.py
@@ -10,6 +10,10 @@ class PanoramaWarping(desc.CommandLineNode):
     commandLine = 'aliceVision_panoramaWarping {allParams}'
     size = desc.DynamicNodeSize('input')
 
+    documentation = '''
+Compute the image warping for each input image in the panorama coordinate system.
+'''
+
     inputs = [
         desc.File(
             name='input',
@@ -21,7 +25,8 @@ class PanoramaWarping(desc.CommandLineNode):
         desc.IntParam(
             name='panoramaWidth',
             label='Panorama Width',
-            description='Panorama width (pixels). 0 For automatic size',
+            description='Panorama Width (in pixels).\n'
+                        'Set 0 to let the software choose the size automatically, so that on average the input resolution is kept (to limit over/under sampling).',
             value=10000,
             range=(0, 50000, 1000),
             uid=[0]
diff --git a/meshroom/nodes/aliceVision/PrepareDenseScene.py b/meshroom/nodes/aliceVision/PrepareDenseScene.py
index 5467d576..afd5b4b2 100644
--- a/meshroom/nodes/aliceVision/PrepareDenseScene.py
+++ b/meshroom/nodes/aliceVision/PrepareDenseScene.py
@@ -9,6 +9,10 @@ class PrepareDenseScene(desc.CommandLineNode):
     parallelization = desc.Parallelization(blockSize=40)
     commandLineRange = '--rangeStart {rangeStart} --rangeSize {rangeBlockSize}'
 
+    documentation = '''
+This node export undistorted images so the depth map and texturing can be computed on Pinhole images without distortion.
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/Publish.py b/meshroom/nodes/aliceVision/Publish.py
index ebe2b9b8..ae49bdd0 100644
--- a/meshroom/nodes/aliceVision/Publish.py
+++ b/meshroom/nodes/aliceVision/Publish.py
@@ -10,6 +10,11 @@ import os
 
 class Publish(desc.Node):
     size = desc.DynamicNodeSize('inputFiles')
+
+    documentation = '''
+This node allows to copy files into a specific folder.
+'''
+
     inputs = [
         desc.ListAttribute(
             elementDesc=desc.File(
diff --git a/meshroom/nodes/aliceVision/SfMAlignment.py b/meshroom/nodes/aliceVision/SfMAlignment.py
index 798ce1c5..3e277bff 100644
--- a/meshroom/nodes/aliceVision/SfMAlignment.py
+++ b/meshroom/nodes/aliceVision/SfMAlignment.py
@@ -7,6 +7,18 @@ class SfMAlignment(desc.CommandLineNode):
     commandLine = 'aliceVision_utils_sfmAlignment {allParams}'
     size = desc.DynamicNodeSize('input')
 
+    documentation = '''
+This node allows to change the coordinate system of one SfM scene to align it on another one.
+
+The alignment can be based on:
+ * from_cameras_viewid: Align cameras in both SfM on the specified viewId
+ * from_cameras_poseid: Align cameras in both SfM on the specified poseId
+ * from_cameras_filepath: Align cameras with a filepath matching, using 'fileMatchingPattern'
+ * from_cameras_metadata: Align cameras with matching metadata, using 'metadataMatchingList'
+ * from_markers: Align from markers with the same Id
+
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/SfMTransfer.py b/meshroom/nodes/aliceVision/SfMTransfer.py
index a30695ca..9be8772b 100644
--- a/meshroom/nodes/aliceVision/SfMTransfer.py
+++ b/meshroom/nodes/aliceVision/SfMTransfer.py
@@ -7,6 +7,10 @@ class SfMTransfer(desc.CommandLineNode):
     commandLine = 'aliceVision_utils_sfmTransfer {allParams}'
     size = desc.DynamicNodeSize('input')
 
+    documentation = '''
+This node allows to transfer poses and/or intrinsics form one SfM scene onto another one.
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/SfMTransform.py b/meshroom/nodes/aliceVision/SfMTransform.py
index 6a9a975a..e65bb56f 100644
--- a/meshroom/nodes/aliceVision/SfMTransform.py
+++ b/meshroom/nodes/aliceVision/SfMTransform.py
@@ -7,6 +7,18 @@ class SfMTransform(desc.CommandLineNode):
     commandLine = 'aliceVision_utils_sfmTransform {allParams}'
     size = desc.DynamicNodeSize('input')
 
+    documentation = '''
+This node allows to change the coordinate system of one SfM scene.
+
+The transformation can be based on:
+ * transformation: Apply a given transformation
+ * auto_from_cameras: Fit all cameras into a box [-1,1]
+ * auto_from_landmarks: Fit all landmarks into a box [-1,1]
+ * from_single_camera: Use a specific camera as the origin of the coordinate system
+ * from_markers: Align specific markers to custom coordinates
+
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/SketchfabUpload.py b/meshroom/nodes/aliceVision/SketchfabUpload.py
index 2a552b87..e599b136 100644
--- a/meshroom/nodes/aliceVision/SketchfabUpload.py
+++ b/meshroom/nodes/aliceVision/SketchfabUpload.py
@@ -51,6 +51,11 @@ def progressUpdate(size=None, progress=None, logManager=None):
 
 class SketchfabUpload(desc.Node):
     size = desc.DynamicNodeSize('inputFiles')
+
+    documentation = '''
+Upload a textured mesh on Sketchfab.
+'''
+
     inputs = [
         desc.ListAttribute(
             elementDesc=desc.File(
diff --git a/meshroom/nodes/aliceVision/StructureFromMotion.py b/meshroom/nodes/aliceVision/StructureFromMotion.py
index c0f960ab..6a00b3cf 100644
--- a/meshroom/nodes/aliceVision/StructureFromMotion.py
+++ b/meshroom/nodes/aliceVision/StructureFromMotion.py
@@ -10,6 +10,59 @@ class StructureFromMotion(desc.CommandLineNode):
     commandLine = 'aliceVision_incrementalSfM {allParams}'
     size = desc.DynamicNodeSize('input')
 
+    documentation = '''
+This node will analyze feature matches to understand the geometric relationship behind all the 2D observations,
+and infer the rigid scene structure (3D points) with the pose (position and orientation) and internal calibration of all cameras.
+The pipeline is a growing reconstruction process (called incremental SfM): it first computes an initial two-view reconstruction that is iteratively extended by adding new views.
+
+1/ Fuse 2-View Matches into Tracks
+
+It fuses all feature matches between image pairs into tracks. Each track represents a candidate point in space, visible from multiple cameras.
+However, at this step of the pipeline, it still contains many outliers.
+
+2/ Initial Image Pair
+
+It chooses the best initial image pair. This choice is critical for the quality of the final reconstruction.
+It should indeed provide robust matches and contain reliable geometric information.
+So, this image pair should maximize the number of matches and the repartition of the corresponding features in each image.
+But at the same time, the angle between the cameras should also be large enough to provide reliable geometric information.
+
+3/ Initial 2-View Geometry
+
+It computes the fundamental matrix between the 2 images selected and consider that the first one is the origin of the coordinate system.
+
+4/ Triangulate
+
+Now with the pose of the 2 first cameras, it triangulates the corresponding 2D features into 3D points.
+
+5/ Next Best View Selection
+
+After that, it selects all the images that have enough associations with the features that are already reconstructed in 3D.
+
+6/ Estimate New Cameras
+
+Based on these 2D-3D associations it performs the resectioning of each of these new cameras.
+The resectioning is a Perspective-n-Point algorithm (PnP) in a RANSAC framework to find the pose of the camera that validates most of the features associations.
+On each camera, a non-linear minimization is performed to refine the pose.
+
+7/ Triangulate
+
+From these new cameras poses, some tracks become visible by 2 or more resected cameras and it triangulates them.
+
+8/ Optimize
+
+It performs a Bundle Adjustment to refine everything: extrinsics and intrinsics parameters of all cameras as well as the position of all 3D points.
+It filters the results of the Bundle Adjustment by removing all observations that have high reprojection error or insufficient angles between observations.
+
+9/ Loop from 5 to 9
+
+As we have triangulated new points, we get more image candidates for next best views selection and we can iterate from 5 to 9.
+It iterates like that, adding cameras and triangulating new 2D features into 3D points and removing 3D points that became invalidated, until we cannot localize new views.
+
+## Online
+[https://alicevision.org/#photogrammetry/sfm](https://alicevision.org/#photogrammetry/sfm)
+'''
+
     inputs = [
         desc.File(
             name='input',
diff --git a/meshroom/nodes/aliceVision/Texturing.py b/meshroom/nodes/aliceVision/Texturing.py
index 117201a8..1e36756f 100644
--- a/meshroom/nodes/aliceVision/Texturing.py
+++ b/meshroom/nodes/aliceVision/Texturing.py
@@ -7,6 +7,20 @@ class Texturing(desc.CommandLineNode):
     commandLine = 'aliceVision_texturing {allParams}'
     cpu = desc.Level.INTENSIVE
     ram = desc.Level.INTENSIVE
+
+    documentation = '''
+This node computes the texturing on the mesh.
+
+If the mesh has no associated UV, it automatically computes UV maps.
+
+For each triangle, it uses the visibility information associated to each vertex to retrieve the texture candidates.
+It select the best cameras based on the resolution covering the triangle. Finally it averages the pixel values using multiple bands in the frequency domain.
+Many cameras are contributing to the low frequencies and only the best ones contributes to the high frequencies.
+
+## Online
+[https://alicevision.org/#photogrammetry/texturing](https://alicevision.org/#photogrammetry/texturing)
+'''
+
     inputs = [
         desc.File(
             name='input',