A PyTorch Library for Accelerating 3D Deep Learning Research
- pytorch
- 3d-deep-learning
- neural-networks
- differentiable-rendering
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A PyTorch Library for Accelerating 3D Deep Learning Research
A python module for scientific analysis of 3D data based on VTK and Numpy
AI drew your research figure. Tavotto makes it publication-ready: edit matplotlib and AI-generated figures on a canvas, lay out multi-panel pages in mm, check journal rules before export. Scripts stay untouched.
Getting serious about Vulkan development with this modern C++ framework, battle-tested in rapid prototyping, research, and teaching. Includes support for real-time ray tracing (RTX), serialization, and meshlets.
Pixel-art diorama playbook and Three.js code kit: research-first build workflow, locked camera, palette snap, ordered dither, inked contour, animated toy-scale cast, per-entity frame QA.
Translated护研搭子 (Huyan Dazi, "nursing research partner") | 18 open-source AI skills for nursing research, covering literature search, study design, data, statistics, writing, and charts and figures; newly added cohort study and RCT workflows, templates, and synthetic examples.
OME (Open Microscopy Environment) develops open-source software and data format standards for the storage and manipulation of biological light microscopy data. A joint project between universities, research establishments and industry in Europe and the USA, OME has over 20 active researchers with strong links to the microscopy community. Funded by private and public research grants, OME has been a major force on the international microscopy stage since 2000.