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[CVPR 2022] "MonoScene: Monocular 3D Semantic Scene Completion": 3D Semantic Occupancy Prediction from a single image
| Date | Stars |
|---|---|
| 2026-07-24 | 815 |
| 2026-07-25 | 815 |
| 2026-07-28 | 816 |
| 2026-07-30 | 816 |
| 2026-08-06 | 816 |
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# MonoScene: Monocular 3D Semantic Scene Completion
**MonoScene: Monocular 3D Semantic Scene Completion**\
[Anh-Quan Cao](https://anhquancao.github.io),
[Raoul de Charette](https://team.inria.fr/rits/membres/raoul-de-charette/)
Inria, Paris, France.
CVPR 2022 \
[](https://arxiv.org/abs/2112.00726)
[](https://astra-vision.github.io/MonoScene/)
[](https://huggingface.co/spaces/CVPR/MonoScene)
If you find this work or code useful, please cite our [paper](https://arxiv.org/abs/2112.00726) and [give this repo a star](https://github.com/astra-vision/MonoScene/stargazers):
```
@inproceedings{cao2022monoscene,
title={MonoScene: Monocular 3D Semantic Scene Completion},
author={Anh-Quan Cao and Raoul de Charette},
booktitle={CVPR},
year={2022}
}
```
# Teaser
|SemanticKITTI | KITTI-360 <br/>(Trained on SemanticKITTI) |
|:------------:|:------:|
|<img src="./teaser/SemKITTI.gif" />|<img src="./teaser/KITTI-360.gif" />|
<p align="center">
<b>NYUv2</b>
</p>
<p align="center">
<img src="./teaser/NYUv2.gif" style="width:48%"/>
</p>
# Table of Content
- [News](#news)
- [Preparing MonoScene](#preparing-monoscene)
- [Installation](#installation)
- [Datasets](#datasets)
- [Pretrained models](#pretrained-models)
- [Running MonoScene](#running-monoscene)
- [Training](#training)
- [Evaluating](#evaluating)
- [Inference & Visualization](#inference--visualization)
- [Inference](#inference)
- [Visualization](#visualization)
- [Related camera-only 3D occupancy prediction projects](#related-camera-only-3d-occupancy-prediction-projects)
- [License](#license)
# News
- 25/03/2026: One step towards generalized unified occupnay prediction with [OccAny: Generalized Unconstrained Urban 3D Occupancy (CVPR'26)](https://valeoai.github.io/OccAny/)
- 05/12/2023: Check out our recent work [PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness](https://astra-vision.github.io/PaSCo/) :rotating_light:
- 20/04/2023: Check out other [camera-only 3D occupancy prediction projects](#related-camera-only-3d-occupancy-prediction-projects)
- 28/06/2022: We added [MonoScene demo on Hugging Face](https://huggingface.co/spaces/CVPR/MonoScene)
- 13/06/2022: We added a tutorial on [How to define viewpoint programmatically in mayavi](https://anhquancao.github.io/blog/2022/how-to-define-viewpoint-programmatically-in-mayavi/)
- 12/06/2022: We added a guide on [how to install mayavi](https://anhquancao.github.io/blog/2022/how-to-install-mayavi-with-python-3-on-ubuntu-2004-using-pip-or-anaconda/)
- 09/06/2022: We fixed the installation errors mentioned in https://github.com/astra-vision/MonoScene/issues/18
# Preparing MonoScene
## Installation
1. Create conda environment:
```
$ conda create -y -n monoscene python=3.7
$ conda activate monoscene
```
2. This code was implemented with python 3.7, pytorch 1.7.1 and CUDA 10.2. Please install [PyTorch](https://pytorch.org/):
```
$ conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.2 -c pytorch
```
3. Install the additional dependencies:
```
$ cd MonoScene/
$ pip install -r requirements.txt
```
4. Install tbb:
```
$ conda install -c bioconda tbb=2020.2
```
5. Downgrade torchmetrics to 0.6.0
```
$ pip install torchmetrics==0.6.0
```
6. Finally, install MonoScene:
```
$ pip install -e ./
```
## Datasets
### SemanticKITTI
1. You need to download
- The **Semantic Scene Completion dataset v1.1** (SemanticKITTI voxel data (700 MB)) from [SemanticKITTI website](http://www.semantic-kitti.org/dataset.html#download)
- The **KITTI Odometry Benchmark calibration data** (Download odometry data set (calibration files, 1 MB)) and the **RGB images** (Download odometry data set (color, 65 GB)) from [KITTI OdoExcerpt of 13,785 characters
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