Tutorial Index¶
Kaolin provides tutorials as ipython notebooks, docs pages and simple scripts. Note that the links point to master.
Detailed Tutorials¶
- Deep Marching Tetrahedra: reconstructs a tetrahedral mesh from point clouds with DMTet, covering:
generating data with Omniverse Kaolin App
loading point clouds from a
.usd
filechamfer distance as a loss function
differentiable marching tetrahedra
using Timelapse API for 3D checkpoints
visualizing 3D results of training
- Understanding Structured Point Clouds (SPCs): walks through SPC features, covering:
under-the-hood explanation of SPC, why it’s useful and key ops
loading a mesh
sampling a point cloud
converting a point cloud to SPC
setting up camera
rendering SPC with ray tracing
storing features in an SPC
- Differentiable Rendering: optimizes a triangular mesh from images using DIB-R renderer, covering:
generating data with Omniverse Kaolin App, and loading this synthetic data
loading a mesh
computing mesh laplacian
DIB-R rasterization
differentiable texture mapping
computing mask intersection-over-union loss (IOU)
using Timelapse API for 3D checkpoints
visualizing 3D results of training
- Fitting a 3D Bounding Box: fits a 3D bounding box around an object in images using DIB-R renderer, covering:
generating data with Omniverse Kaolin App, and loading this synthetic data
loading a mesh
DIB-R rasterization
computing mask intersection-over-union loss (IOU)
- 3D Checkpoint Visualization: explains saving 3D checkpoints and visualizing them, covering:
using Timelapse API for writing 3D checkpoints
understanding output file format
visualizing 3D checkpoints using Omniverse Kaolin App
visualizing 3D checkpoints using bundled
kaolin-dash3d
commandline utility
- Reconstructing Point Cloud with DMTet: Trains an SDF estimator to reconstruct a mesh from a point cloud covering:
using point clouds data generated with Omniverse Kaolin App
loading point clouds from an USD file.
defining losses and regularizer for a mesh with point cloud ground truth
applying marching tetrahedra
using Timelapse API for 3D checkpoints
visualizing 3D checkpoints using
kaolin-dash3d
Simple Recipes¶
- I/O and Data Processing:
usd_kitchenset.py: loading multiple meshes from a
.usd
file and savingspc_from_pointcloud.py: converting a point cloud to SPC object
occupancy_sampling.py: computing occupancy function of points in a mesh using
check_sign
spc_basics.py: showing attributes of an SPC object
spc_dual_octree.py: computing and explaining the dual of an SPC octree
spc_trilinear_interp.py: computing trilinear interpolation of a point cloud on an SPC
- Visualization:
visualize_main.py: using Timelapse API to write mock 3D checkpoints