MonoScene: Monocular 3D Semantic Scene Completion] [arXiv + supp] | [Project page]
Anh-Quan Cao,
Raoul de Charette
Inria, Paris, France. CVPR 2022.
If you find this work useful, please cite our paper:
@inproceedings{cao2022monoscene,
title={MonoScene: Monocular 3D Semantic Scene Completion},
author={Anh-Quan Cao and Raoul de Charette},
booktitle={CVPR},
year={2022}
}
| SemanticKITTI | KITTI-360 (Trained on SemanticKITTI) |
|---|---|
 |
 |
NYUv2
- Create conda environment:
$ conda create -y -n monoscene python=3.7
$ conda activate monoscene
- This code was implemented with python 3.7, pytorch 1.7.1 and CUDA 10.2. Please install PyTorch:
$ conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.2 -c pytorch
- Install the additional dependencies:
$ cd MonoScene/
$ pip install -r requirements.txt
- Install tbb:
$ conda install -c bioconda tbb=2020.2
- Finally, install MonoScene:
$ pip install -e ./
-
You need to download
- The Semantic Scene Completion dataset v1.1 (SemanticKITTI voxel data (700 MB)) from SemanticKITTI website
- 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 Odometry website.
-
Create a folder to store SemanticKITTI preprocess data at
/path/to/kitti/preprocess/folder. -
Store paths in environment variables for faster access (Note: folder 'dataset' is in /path/to/semantic_kitti):
$ export KITTI_PREPROCESS=/path/to/kitti/preprocess/folder
$ export KITTI_ROOT=/path/to/semantic_kitti
- Preprocess the data to generate labels at a lower scale, which are used to compute the ground truth relation matrices:
$ cd MonoScene/
$ python monoscene/data/semantic_kitti/preprocess.py kitti_root=$KITTI_ROOT kitti_preprocess_root=$KITTI_PREPROCESS
-
Download the NYUv2 dataset.
-
Create a folder to store NYUv2 preprocess data at
/path/to/NYU/preprocess/folder. -
Store paths in environment variables for faster access:
$ export NYU_PREPROCESS=/path/to/NYU/preprocess/folder
$ export NYU_ROOT=/path/to/NYU/depthbin
- Preprocess the data to generate labels at a lower scale, which are used to compute the ground truth relation matrices:
$ cd MonoScene/
$ python monoscene/data/NYU/preprocess.py NYU_root=$NYU_ROOT NYU_preprocess_root=$NYU_PREPROCESS
-
We only perform inference on KITTI-360. You can download either the Perspective Images for Train & Val (128G) or the Perspective Images for Test (1.5G) at http://www.cvlibs.net/datasets/kitti-360/download.php.
-
Create a folder to store KITTI-360 data at
/path/to/KITTI-360/folder. -
Store paths in environment variables for faster access:
$ export KITTI_360_ROOT=/path/to/KITTI-360
Download MonoScene pretrained models on SemanticKITTI and on NYUv2, then put them in the folder /path/to/MonoScene/trained_models.
To train MonoScene with SemanticKITTI, type:
-
Create folders to store training logs at /path/to/kitti/logdir.
-
Store in an environment variable:
$ export KITTI_LOG=/path/to/kitti/logdir
- Train MonoScene using 4 GPUs with batch_size of 4 (1 item per GPU) on Semantic KITTI:
$ cd MonoScene/
$ python monoscene/scripts/train_monoscene.py \
dataset=kitti \
enable_log=true \
kitti_root=$KITTI_ROOT \
kitti_preprocess_root=$KITTI_PREPROCESS\
kitti_logdir=$KITTI_LOG \
n_gpus=4 batch_size=4
-
Create folders to store training logs at /path/to/NYU/logdir.
-
Store in an environment variable:
$ export NYU_LOG=/path/to/NYU/logdir
- Train MonoScene using 2 GPUs with batch_size of 4 (2 item per GPU) on NYUv2:
$ cd MonoScene/
$ python monoscene/scripts/train_monoscene.py \
dataset=NYU \
NYU_root=$NYU_ROOT \
NYU_preprocess_root=$NYU_PREPROCESS \
logdir=$NYU_LOG \
n_gpus=2 batch_size=4
To evaluate MonoScene on SemanticKITTI validation set, type:
$ cd MonoScene/
$ python monoscene/scripts/eval_monoscene.py \
dataset=kitti \
kitti_root=$KITTI_ROOT \
kitti_preprocess_root=$KITTI_PREPROCESS \
n_gpus=1 batch_size=1
To evaluate MonoScene on NYUv2 test set, type:
$ cd MonoScene/
$ python monoscene/scripts/eval_monoscene.py \
dataset=NYU \
NYU_root=$NYU_ROOT\
NYU_preprocess_root=$NYU_PREPROCESS \
n_gpus=1 batch_size=1
Please create folder /path/to/monoscene/output to store the MonoScene outputs and store in environment variable:
export MONOSCENE_OUTPUT=/path/to/monoscene/output
To generate the predictions on the NYUv2 test set, type:
$ cd MonoScene/
$ python monoscene/scripts/generate_output.py \
+output_path=$MONOSCENE_OUTPUT \
dataset=NYU \
NYU_root=$NYU_ROOT \
NYU_preprocess_root=$NYU_PREPROCESS \
n_gpus=1 batch_size=1
To generate the predictions on the Semantic KITTI validation set, type:
$ cd MonoScene/
$ python monoscene/scripts/generate_output.py \
+output_path=$MONOSCENE_OUTPUT \
dataset=kitti \
kitti_root=$KITTI_ROOT \
kitti_preprocess_root=$KITTI_PREPROCESS \
n_gpus=1 batch_size=1
Here we use the sequence 2013_05_28_drive_0009_sync, you can use other sequences. To generate the predictions on KITTI-360, type:
$ cd MonoScene/
$ python monoscene/scripts/generate_output.py \
+output_path=$MONOSCENE_OUTPUT \
dataset=kitti_360 \
+kitti_360_root=$KITTI_360_ROOT \
+kitti_360_sequence=2013_05_28_drive_0009_sync \
n_gpus=1 batch_size=1
We use mayavi to visualize the predictions. Please install mayavi following the official installation instruction. Then, use the following commands to visualize the outputs on respective datasets.
If you have trouble installing mayavi, the following comments might help:
$ cd MonoScene/
$ python monoscene/scripts/visualization/NYU_vis_pred.py +file=/path/to/output/file.pkl
$ cd MonoScene/
$ python monoscene/scripts/visualization/kitti_vis_pred.py +file=/path/to/output/file.pkl +dataset=kitt
$ cd MonoScene/
$ python monoscene/scripts/visualization/kitti_vis_pred.py +file=/path/to/output/file.pkl +dataset=kitti_360
MonoScene is released under the Apache 2.0 license.