RENDR Dataset

Dataset Description

RENDR is a large-scale synthetic dataset of rendered 3D objects across 11 object categories. The dataset contains rendered images from 3D assets sourced from BlenderKit and Haven, designed for training and evaluating computer vision models on synthetic 3D data.

Dataset Statistics

Split Overview

Split	Total Images	Rendered	BlenderKit Assets	Haven Assets
Train	29,291	23,836	5,397	58
Test	5,929	4,206	1,701	22

Class Distribution

Class	Train (Rendered)	Train (BlenderKit)	Train (Haven)	Test (Rendered)	Test (BlenderKit)	Test (Haven)
Animals	2,369	133	1	416	103	1
Appliances	1,966	388	5	346	150	2
Architecture	2,224	523	7	392	171	3
Decoration	2,226	731	0	392	188	0
Electronics	1,905	246	6	336	126	3
Furniture	2,154	1,075	0	380	190	0
Lighting	1,565	266	1	278	117	0
Mechanical	2,150	386	18	380	151	8
Nature	2,782	799	0	492	217	0
People	2,554	205	0	452	136	0
Tools	1,941	645	20	342	152	5

Dataset Structure

rendr/
├── train/
│   ├── animals/
│   ├── appliances/
│   ├── architecture/
│   ├── decoration/
│   ├── electronics/
│   ├── furniture/
│   ├── lighting/
│   ├── mechanical/
│   ├── nature/
│   ├── people/
│   └── tools/
└── test/
    └── [same structure as train]

Normalization Statistics

For standard ImageNet-style normalization:

• Mean: [0.5910, 0.5846, 0.5790]
• Std: [0.2724, 0.2733, 0.2781]

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("jneuendorf/rendr")

# Access splits
train_data = dataset['train']
test_data = dataset['test']

# Example: Load with normalization
from torchvision import transforms

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.5910, 0.5846, 0.5790],
        std=[0.2724, 0.2733, 0.2781]
    )
])

Data Sources

• Rendered Images: Custom rendered synthetic images
• BlenderKit: 3D assets from BlenderKit library
• Haven: 3D assets from Poly Haven

Classes

The dataset includes 11 object categories:

1. Animals
2. Appliances
3. Architecture
4. Decoration
5. Electronics
6. Furniture
7. Lighting
8. Mechanical
9. Nature
10. People
11. Tools

Citation

If you use this dataset, please cite:

@dataset{rendr,
  title={RENDR: A Large-Scale Synthetic 3D Object Dataset},
  author={Jim Neuendorf},
  year={2025}
}

License

MIT License - Copyright (c) 2025 Jim Neuendorf