diff --git a/_projects/renders.md b/_projects/renders.md
new file mode 100644
index 0000000..70313bc
--- /dev/null
+++ b/_projects/renders.md
@@ -0,0 +1,180 @@
+---
+layout: page
+title: Renders
+description: Video rendering of LiDAR point clouds and 3D images.
+img: /assets/img/republique-480.webp
+importance: 3
+category: thesis
+---
+
+I made lots of videos to illustrate my slides during my PhD! You can see
+some of them below, and how I made them.
+
+## Point clouds
+
+To visualize and render point clouds videos I used [CloudCompare].
+CloudCompare is very versatile to visualize and manipulate 3D point
+clouds and comes with a large number of plugins. I used it to create
+videos like this one:
+
+[CloudCompare]: https://www.cloudcompare.org/
+
+![Video: 3D point cloud of
+Rennes.](/assets/vid/1080_x264_crf35.mp4){.figure-img .img-fluid
+.rounded .z-depth-1 loop=true}
+
+On this video you can see a point cloud with shading and color. The
+colors represent the LiDAR intensity, using a color map ranging from
+purple for low intensities, to yellow for high intensities. The shading
+allow a better perception of the 3D depth of the point cloud. I used
+*Portion de Ciel Visible* (PCV) to compute this shading. Unfortunately,
+CloudCompare allow only one color scalar field  to be displayed at a
+time. So I rendered the frames with PCV, then a second time with
+intensities. I then produced composite images with the PCV as luminance
+and the intensity as chroma with this script:
+
+
+```bash
+#!/bin/env bash
+
+LUMA_FRAMES=$1
+CHROMA_FRAMES=$2
+OUT_DIR=$3
+
+CRF=10
+SIZE='1920x1080'
+
+mkdir -p $OUT_DIR
+
+# Compose luma and chroma frames
+
+for frame in $(ls $LUMA_FRAMES/*.png); do
+    fname=$(basename $frame)
+    echo -en "\rProcessing $fname..."
+    if ! montage $LUMA_FRAMES/$fname $CHROMA_FRAMES/$fname -geometry +25+0 $OUT_DIR/$fname
+    then
+        echo 'Error somehow'
+        exit 2
+    fi
+done
+
+# Resize and crop frames
+
+mogrify -path $OUT_DIR \
+        -alpha off \
+        -resize $SIZE^ \
+        -gravity Center \
+        -extent $SIZE \
+        $IN_DIR/*
+
+# Encode video
+
+ffmpeg -r 50 \
+       -i $OUT_DIR/frame_000%3d.png \
+       -c:v libx264 \
+       -crf $CRF \
+       -preset veryslow \
+       -pix_fmt yuv420p \
+       x264_crf${CRF}.mp4
+
+```
+
+## Voxels
+
+I tried a lot of different software to visualise and render voxels, but
+nothing really convinced me. I brought out the big guns and went back to
+basics!
+For the rendering of the voxels I used [Blender] with the Cycles
+renderer.
+
+[Blender]: https://www.blender.org/
+
+![Video: 3D image of the same dataset.](/assets/vid/parlement_c20.mp4){.figure-img
+.img-fluid .rounded .z-depth-1 loop=true}
+
+Here you can see a voxelization of the previous point cloud. The colors
+represent the LiDAR intensity, using the same color map ranging from
+purple for low LiDAR intensities, to yellow for high intensities. The
+colors are shaded with a virtual sun by using a realist ray-tracing and
+a logarithmic exposure sensitivity. The colors appear a little washed
+out, as they would be on a real camera under similar lighting
+conditions.
+
+Nothing really provide support to open voxels in Blender. So I write
+this Python script to open voxel files from my [Idefix Python
+package][idefix] directly in Blender. This script is a quick draft and
+would need a good refactoring, but hey! It works!
+
+```python
+import bpy
+from matplotlib import pyplot as plt
+import numpy as np
+
+data_in = 'voxels.npz'
+data = np.load(data_in)
+
+coords = data['coords']
+vmin, vmax = np.quantile(data['intensity'], (0.01, 0.99))
+colors = ((np.clip(data['intensity'], vmin, vmax) 
+           - vmin) 
+           / (vmax - vmin) 
+           * 255).astype(np.int)
+
+
+def gen_cmap(mesh, name='viridis'):
+    cm = plt.get_cmap(name)
+    for i in range(256):
+        mat = bpy.data.materials.new(name)
+        mat.diffuse_color = cm(i)
+        #mat.specular_color = cm(i)
+        mesh.materials.append(mat)
+
+    
+def gen_voxels(coords, colors):
+
+    # make mesh
+
+    vertices_base = np.array(((0, 0, 0), (0, 1, 0), (1, 1, 0), (1, 0, 0), 
+                              (0, 0, 1), (0, 1, 1), (1, 1, 1), (1, 0, 1)))
+    faces_base = np.array([(0,1,2,3), (7,6,5,4), (7,4,0,3), 
+                           (6,7,3,2), (5,6,2,1), (4,5,1,0)])
+
+    vxl_count = coords.shape[0]
+
+    vertices = (coords[None].repeat(8, axis=0).swapaxes(1, 0) 
+                + vertices_base).reshape(-1, 3)
+    faces = (faces_base[None].repeat(vxl_count, axis=0) 
+             + (np.arange(vxl_count) 
+             * 8)[None].repeat(6, axis=0).T[...,None]).reshape(-1, 4)
+    colors = colors.repeat(6)
+
+    new_mesh = bpy.data.meshes.new('vxl_mesh')
+    new_mesh.from_pydata(vertices, [], faces.tolist())
+    new_mesh.update()
+
+    # make object from mesh
+
+    new_object = bpy.data.objects.new('vxl_object', new_mesh)
+
+    # make collection
+
+    new_collection = bpy.data.collections.new('vxl_scene')
+    bpy.context.scene.collection.children.link(new_collection)
+
+    # add object to scene collection
+
+    new_collection.objects.link(new_object)
+
+    # Set colors
+
+    gen_cmap(new_mesh)
+    new_object.data.polygons.foreach_set('material_index', colors)
+
+
+gen_voxels(coords, colors)
+```
+
+
+![](/assets/img/republique.png){.img-fluid .rounded .z-depth-1}
+
+[idefix]: https://github.com/fguiotte/idefix/
diff --git a/_projects/sap.md b/_projects/sap.md
index 811dc06..787540f 100644
--- a/_projects/sap.md
+++ b/_projects/sap.md
@@ -8,7 +8,7 @@ category: thesis
 ---
 
 SAP (for Simple Attribute Profiles) is a Python package to easily
-compute attribute profiles of images. I have developed this package as
+compute *attribute profiles* of images. I have developed this package as
 part of my PhD thesis.
 
 The source code is available on [github][git]. I used this project to
diff --git a/_projects/spectra.md b/_projects/spectra.md
index e6c18d1..7007247 100644
--- a/_projects/spectra.md
+++ b/_projects/spectra.md
@@ -2,7 +2,7 @@
 layout: page
 title: Spectra
 description: Application using the morphological hierarchies and LiDAR data.
-img: /assets/img/spectra.png
+img: /assets/img/spectra-480.webp
 importance: 1
 category: thesis
 ---
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