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 # Calculate signed distance fields for arbitrary meshes
 
-This project calculates SDFs for meshes.
+This project calculates approximate SDFs for meshes.
 It works for **non-watertight** meshes (meshes with holes), **self-intersecting** meshes, meshes with **non-manifold geometry** and meshes with **inconsistently oriented faces**.
 
+## Examples
+
+### Voxelize a mesh
+
+The `mesh_to_voxels` function creates an N ✕ N ✕ N array of SDF values.
+In this example, a mesh reconstructed using Marching Cubes and then rendered.
+
+```python
+from mesh_to_sdf import mesh_to_voxels
+
+import trimesh
+import skimage
+
+VOXEL_RESOLUTION = 64
+
+mesh = trimesh.load('chair.obj')
+
+voxels = mesh_to_voxels(mesh, VOXEL_RESOLUTION, pad=True)
+
+vertices, faces, normals, _ = skimage.measure.marching_cubes_lewiner(voxels, level=0, spacing=(2 / VOXEL_RESOLUTION, 2 / VOXEL_RESOLUTION, 2 / VOXEL_RESOLUTION))
+mesh = trimesh.Trimesh(vertices=vertices, faces=faces, vertex_normals=normals)
+mesh.show()
+```
+![Example of a mesh and a reconstructed SDF voxel volume](example/voxel.png)
+
+### Sample SDF points non-uniformly near the surface
+
+This example creates 250,000 points, where most of the points are close to the surface and some are sampled uniformly.
+This is the method that is proposed and used in the [DeepSDF paper](https://arxiv.org/abs/1901.05103).
+In this example, the resulting points are rendered in red where the SDF is positive and in blue where it is negative.
+
+```python
+from mesh_to_sdf import sample_sdf_near_surface
+
+import trimesh
+import pyrender
+import numpy as np
+
+mesh = trimesh.load('chair.obj')
+
+points, sdf = sample_sdf_near_surface(mesh, number_of_points=250000)
+
+colors = np.zeros(points.shape)
+colors[sdf < 0, 2] = 1
+colors[sdf > 0, 0] = 1
+cloud = pyrender.Mesh.from_points(points, colors=colors)
+scene = pyrender.Scene()
+scene.add(cloud)
+viewer = pyrender.Viewer(scene, use_raymond_lighting=True, point_size=2)
+```
+
+![Example of a mesh and a point cloud of non-uniformly sampled SDF points](example/non-uniform.png)
+
+## How it works
+
 The general pipeline for calculating SDF in this project is as follows:
 
-1. Create virtual laser scans of the shape from multiple angles
+1. Create 100 virtual laser scans of the shape from multiple angles.
+These each consist of a normal buffer and a depth buffer.
 2. Use the inverse MVP matrix and depth buffer of each scan to calculate a world-space surface point cloud
 3. Determine the value of the SDF for each query point by finding the closest surface point using a kd-tree
-4. Determine the sign of the SDF using either the normal of the closest surface point or by checking it against the depth buffers of the scans
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+4. Determine the sign of the SDF using either the normal of the closest surface point or by checking it against the depth buffers of the scans.
+When using normals, the sign is determined with a dot product.
+When using the depth buffer method, the point is projected in the frame of each render.
+By comparing the depth element of the depth buffer and depth of the query point, we determine if the query point is seen by the camera.
+The sign of the point is positive if it is seen by any of the cameras.
+
+This repository contains an implementation of the procedure proposed in the [DeepSDF paper](), as well as some alternatives.
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