Sergei Zobov's repositories
OctoPrint-Telegram
Plugin for octoprint to send status messages and receive commands via Telegram messenger.
Network-Simplex
A Python implementation of the Network Simplex algorithm applied to the shortest path problem.
Robotics-Berlin.github.io
webpage
szobov.github.io
Personal blog
bottle
bottle.py is a fast and simple micro-framework for python web-applications.
emacs-libvterm
Emacs libvterm integration
gatery
Gatery, a library for circuit design.
Meshtastic-device
Device code for the Meshtastic ski/hike/fly/customizable open GPS radio
pip-tools
A set of tools to keep your pinned Python dependencies fresh.
PX4-Firmware
PX4 Autopilot Software
pytest-parallel
A pytest plugin for parallel and concurrent testing
pytransform3d
3D transformations for Python.
pyvis
Python package for creating and visualizing interactive network graphs.
telegram-list
List of telegram groups, channels & bots // Список интересных групп, каналов и ботов телеграма // Список чатов для программистов
trigger-circleci-pipeline-action
Trigger a CircleCI pipeline from any GitHub Actions event.
VCMeshConv
Learning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher precision than traditional methods, they remain unable to capture fine-grained deformations. Furthermore, these methods can only be applied to a template-specific surface mesh, and is not applicable to more general meshes, like tetrahedrons and non-manifold meshes. While more general graph convolution methods can be employed, they lack performance in reconstruction precision and require higher memory usage. In this paper, we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents presented by irregular mesh connections. Our model outperforms state-of-the-art methods on reconstruction accuracy. In addition, the latent codes of our network are fully localized thanks to the fully convolutional structure, and thus have much higher interpolation capability than many traditional 3D mesh generation models.