MMehdiMousavi/SuperCaustics
Real-time, open-source simulation of transparent objects for deep learning applications
This tool helps researchers and AI engineers create realistic, synthetic image datasets featuring transparent objects, which are notoriously difficult for computer vision systems to interpret. You input 3D models of objects and backgrounds, then the tool simulates scenes, producing a large volume of annotated image data ready for training deep learning models. It's designed for those building and testing computer vision models that need to accurately "see" and interact with clear or reflective items.
No commits in the last 6 months.
Use this if you need to generate extensive and diverse image datasets of transparent objects for training computer vision models, especially when real-world data collection is impractical or insufficient.
Not ideal if you are looking for a simple drag-and-drop tool for occasional image generation without deep learning applications, or if you don't have access to high-end NVIDIA RTX hardware.
Stars
59
Forks
13
Language
Python
License
—
Category
Last pushed
Jan 22, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/MMehdiMousavi/SuperCaustics"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
pypose/pypose
A library for differentiable robotics on manifolds.
MarcoForte/FBA_Matting
Official repository for the paper F, B, Alpha Matting
snap-research/articulated-animation
Code for Motion Representations for Articulated Animation paper
foamliu/Deep-Image-Matting
Deep Image Matting
dyelax/Adversarial_Video_Generation
A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by...