Stars
A lightweight Python library for visualizing camera poses, fields of view, coordinate axes, and geometric primitives in 3D using Matplotlib.
Torch MinGRU implementation based on "Were RNNs All We Needed?"
A vectorized n-dimensional ICP implementation for registering two point clouds or a point cloud with a polyline.
Depth map compression by colorization in vectorized form
Image stitching of planar targets based on analytical homographies
A pure PyTorch based implementation of "Instant Neural Graphics Primitives with a Multiresolution Hash Encoding" with tweaks.
Real Spherical Harmonics for PyTorch
Minimal, standalone, fast Python OpenEXR reader for single-part, uncompressed scan-line files as produced by Blender.
A modern Python library to work with Anoto dot patterns.
💦 Seamless, distributed, real-time integration of Blender into PyTorch data pipelines
Theory and implementation of Monte Carlo integration techniques
Vectorized Python methods for creating, manipulating and tessellating signed distance fields.
Seamlessly integrate numpy arrays into pydantic models.
Temporal blending of projectile motion estimates in 1D
toy examples glow and planar flow
Fitting kinematic parameters to best align with set of noisy anchor points in Python.
Repository for Marp Themes created with beauty and simplicity in mind.
Implementation of "Learning to Reach Goals via Iterated Supervised Learning"
Generates psychedelic color textures in the spirit of Blender's magic texture shader using Python/Numpy
A PyTorch-Lightning callback to increase model reproducibility through enforcing consistent git repository states upon training and validation.
Python implementation of "Global Data Association for MOT Tracking using Network Flows"
Non-dimensionalization of physical equations using sympy.
Dimensional analysis and modeling in Python
Python code to compute 3D parametric supershapes; additional Blender mesh generation support
Latex, TikZ calibration pattern generation.
Convolutional PyTorch debayering / demosaicing layers
Code for 'Notes on Semi-Supervised Expectation Maximization'