GitHub link:

https://github.com/astra-vision/LMSCNet

configuration can be changed under:

SSC_configs/config_routine.py

link:

https://www.rocq.inria.fr/rits_files/computer-vision/lmscnet/semanticKITTI_v1.1_dscale.zip /home/student/semantic_scene/LMSCNet

data preprocess

python LMSCNet/data/labels_downscale.py --dset_root /home2/tmp/kiran/2DPASS/

training

python LMSCNet/train.py --cfg SSC_configs/examples/LMSCNet.yaml --dset_root /home2/tmp/kiran/2DPASS/

the model will be saved under /home/student/semantic_scene/SSC_out, the parameter setting is in /home/student/semantic_scene/LMSCNet/SSC_configs

validate

python LMSCNet/validate.py --weights ../LMSCNet.pth --dset_root /home2/tmp/kiran/2DPASS/

Official repository.

LMSCNet: Lightweight Multiscale 3D Semantic Completion
Luis Roldão, Raoul de Charette, Anne Verroust-Blondet
Inria, Akka Research. 3DV 2020 (oral)
[Demo Video]

If you find our work useful, please cite:

@inproceedings{roldao2020lmscnet,
  title={LMSCNet: Lightweight Multiscale 3D Semantic Completion},
  author={Rold{\~a}o, Luis and de Charette, Raoul and Verroust-Blondet, Anne},
  booktitle={International Conference on 3D Vision (3DV)},
  year={2020}
}

Tested with

• PyTorch 1.3.1
• CUDA 10.2
• Python 3.7.5
• Numpy 1.17.4

We advise to create a new conda environment for installation.

$ conda create --name lmscnet_ssc python=3.7.5 numpy tqdm scipy scikit-learn pyyaml imageio tensorboard -y
$ conda activate lmscnet_ssc
$ conda install pytorch torchvision cudatoolkit=10.0 -c pytorch

Then clone this repository in desired location

$ git clone https://github.com/cv-rits/LMSCNet

Please download the Full Semantic Scene Completion dataset (v1.1) from the SemanticKITII website and extract it.

You need to preprocess the data to generate lower scale labels for LMSCNet first. The preprocessing performs majority pooling over high-resolution original scale label grids (1:1) in order to obtain ground-truth data at lower resolutions (1:2, 1:4 and 1:8). It also generates validity masks as such resolutions to consider the loss on known voxels only, as in original scale data. All information will be stored in the same format and respective location than the semanticKITTI provided data with new file extensions (file.label_1_X and file.invalid_1_X).

If you are using v1.1 of the dataset, you can download the data directly from here. Please extract the data into the semanticKITTI root folder.

Otherwise, you need to generate the data by running the LMSCNet/data/labels_downscale.py as follows:

$ cd <root dir of this repo>
$ python LMSCNet/data/labels_downscale.py --dset_root <path/dataset/root>

• dset_root should point to the root directory of the SemanticKITTI dataset (containing dataset folder)

All training settings can be edited by using the yaml file generator in SSC_configs/config_routine.py. We provide training routine examples in the SSC_configs/examples/ folder. Make sure to change the dataset path to your extracted dataset location in such files if you want to use them for training. Additionally, you can change the folder where the performance and stats will be stored, this folder has been defined as SSC_out by default.

• config_dict['DATASET']['ROOT_DIR'] should be changed to the root directory of the SemanticKITTI dataset (containing dataset folder)
• config_dict['OUTPUT']['OUT_ROOT'] to be changed to desired output folder.

You can run the training with

$ cd <root dir of this repo>
$ python LMSCNet/train.py --cfg SSC_configs/examples/LMSCNet.yaml --dset_root <path/dataset/root>

We also provide single scale version of our network which can achieve slightly better performance at the cost of losing multiscale capacity:

$ cd <root dir of this repo>
$ python LMSCNet/train.py --cfg SSC_configs/examples/LMSCNet_SS.yaml --dset_root <path/dataset/root>

Train coded baselines with:

$ python LMSCNet/train.py --cfg SSC_configs/examples/SSCNet.yaml --dset_root <path/dataset/root>
$ python LMSCNet/train.py --cfg SSC_configs/examples/SSCNet_full.yaml --dset_root <path/dataset/root>

In all previous examples you need to provide your path to the dataset folder, if not provided, the path set in the .yaml file will be used by default.

Validation passes are done during training routine. Additional pass in the validation set with saved model to check performance can be done by using the LMSCNet/validate.py file. You need to provide the path to the saved model and the dataset root directory.

$ cd <root dir of this repo>
$ python LMSCNet/validate.py --weights </path/to/model.pth> --dset_root <path/dataset/root>

Since SemantiKITTI contains a hidden test set, we provide test routine to save predicted output in same format of SemantiKITTI, which can be compressed and uploaded to the SemanticKITTI Semantic Scene Completion Benchmark. We recommend to pass compressed data through official checking script provided in the SemanticKITTI Development Kit to avoid any issue. You can provide which checkpoints you want to use for testing. We used the ones that performed best on the validation set during training. You need to provide the path to the saved model, the dataset root directory and the output path to where the predictions will be stored.

$ cd <root dir of this repo>
$ python LMSCNet/test.py --weights </path/to/model.pth> --dset_root <path/dataset/root> --out_path <predictions/output/path>

We test the robustness of our network against sparsity by retrieving the original 64-layers KITTI scans used in SemanticKITTI and subsampling 8/16/32 layers LiDARs with layers subsampling.

coming up soon...

You can download the models with the scores below from this Google drive folder.

^* Results reported correspond to SemanticKITTI hidden test set V1.0. SemanticKITTI benchmark has recently changed to v1.1 due to grid shift issue (link). This should bring also slight differences if re-uploaded to test benchmark.

LMSCNet is released under the Apache 2.0 license.