sdftoolbox

This repository provides vectorized Python methods for creating, manipulating and tessellating signed distance fields (SDFs). This library was started to investigate variants of dual isosurface extraction methods, but has since evolved into a useful toolbox around SDFs.

The image above shows two reconstructions of a sphere displaced by waves. The reconstruction on the left uses (dual) SurfaceNets from this library, the right side shows the result of applying (primal) Marching Cubes algorithm from scikit-image.

See examples/compare.py for details and doc/SDF.md for an in-depth documentation.

Features

A generic blueprint algorithm for dual iso-surface generation from SDFs
- providing the following vertex placement strategies
  - (Naive) SurfaceNets
  - Dual Contouring
  - Midpoint to generate Minecraft like reconstructions
- providing the following edge/surface boundary intersection strategies
  - Linear (single step)
  - Newton (iterative)
  - Bisection (iterative)
Mesh postprocessing
- Vertex reprojection onto SDFs
- Quad/Triangle topology support
- Vertex/Face normal support
Tools for programmatically creating and modifying SDFs
Importing volumetric SDFs from NeRF tools such as instant-ngp and NeuS2
Inline plotting support for reconstructed meshes using matplotlib
Exporting (STL) of tesselated isosurfaces

Documentation

Algorithmic ideas, mathematical details and results are discussed in doc/SDF.md.

Example Code

# Main import
import sdftoolbox

# Setup a snowman-scene
snowman = sdftoolbox.sdfs.Union(
    [
        sdftoolbox.sdfs.Sphere.create(center=(0, 0, 0), radius=0.4),
        sdftoolbox.sdfs.Sphere.create(center=(0, 0, 0.45), radius=0.3),
        sdftoolbox.sdfs.Sphere.create(center=(0, 0, 0.8), radius=0.2),
    ],
)
family = sdftoolbox.sdfs.Union(
    [
        snowman.transform(trans=(-0.75, 0.0, 0.0)),
        snowman.transform(trans=(0.0, -0.3, 0.0), scale=0.8),
        snowman.transform(trans=(0.75, 0.0, 0.0), scale=0.6),
    ]
)
scene = sdftoolbox.sdfs.Difference(
    [
        family,
        sdftoolbox.sdfs.Plane().transform(trans=(0, 0, -0.2)),
    ]
)

# Generate the sampling locations. Here we use the default params
grid = sdftoolbox.Grid(
    res=(65, 65, 65),
    min_corner=(-1.5, -1.5, -1.5),
    max_corner=(1.5, 1.5, 1.5),
)

# Extract the surface using dual contouring
verts, faces = sdftoolbox.dual_isosurface(
    scene,
    grid,
    vertex_strategy=sdftoolbox.NaiveSurfaceNetVertexStrategy(),
    triangulate=False,
)

generates

See examples/hello_dualiso.py for details.

Install

Install with development extras to run all the examples.

pip install git+https://github.com/cheind/sdf-surfacenets#egg=sdf-surfacenets[dev]

Examples

The examples can be found in ./examples/. Each example can be invoked as a module

python -m examples.<name>

Mesh from NeRF

This library supports loading SDFs from density fields provided by various NeRF implementations (instant-ngp and NeuS2). These discretized SDF fields can then be manipulated/triangulated using sdftoolbox routines.

The following image shows the resulting triangulated mesh from the Lego scene generated by sdftoolbox from an instant-ngp density image atlas.

Command

python -m examples.nerf2mesh        \
    -r 256 -t 4.0 -o -1.5 -s 0.0117 \
    --sdf-flip                      \
    doc/nerf/density.png

# Use 'python -m examples.nerf2mesh --help' for more options.

Note, meshes generated by sdftoolbox seem to be of better quality than those generated by the respective NeRF tools. The following compares a low-res reconstruction (res=64,drange=1) of the same scene