ShapeNet Rendering
The ShapeNet Rendering dataset offers a vast corpus of images from the 3D rendering of modelled objects. These images are meant to train models for computer vision, image synthesis, and other AI applications.
Approximately 524,600 images, 4.7 GB, image format (PNG/JPEG depending on source)
Apache 2.0
Description
ShapeNet Rendering contains over 500,000 images generated by 3D rendering of various objects. These images facilitate the learning of computer vision models and the generation of realistic synthetic images.
What is this dataset for?
- Training 3D recognition models and computer vision
- Research in image synthesis and realistic rendering
- Virtual and augmented reality applications
Can it be enriched or improved?
This dataset can be supplemented by other renderings from different angles, resolutions, or lighting conditions. Adding specific annotations (object categories, segmentation) can also increase its value.
🔎 In summary
🧠 Recommended for
- 3D vision researchers
- VR/AR projects
- Image-generative AI
🔧 Compatible tools
- PyTorch
- TensorFlow
- OpenCV
- Blender
💡 Tip
Use image augmentation techniques to enrich the variety of renderings during training.
Frequently Asked Questions
What types of objects are included in this dataset?
The dataset contains various 3D models representing common objects, but the exact list depends on the dataset version.
Does this dataset include detailed annotations?
Annotations are basic, mostly images without fine segmentation.
Can this dataset be used to train image synthesis models?
Yes, it's an excellent dataset for synthesizing and rendering realistic images from 3D models.
