Skip to content
This work is based on our paper Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds, which is appeared at the IEEE International Conference on Computer Vision (ICCV) 2017, 3DRMS Workshop.
Branch: master
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information.
batch_generators initial commit Oct 5, 2018
datasets better error message if dataset could not be found Oct 19, 2018
doc initial commit Oct 5, 2018
experiments/iccvw_paper_2017 adapt experiment config files to new framework Oct 19, 2018
models initial commit Oct 5, 2018
optimizers initial commit Oct 5, 2018
tools initial commit Oct 5, 2018
.gitignore initial commit Oct 5, 2018
LICENSE Update LICENSE Oct 5, 2018 Update Oct 15, 2018 initial commit Oct 5, 2018

Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds

Created by Francis Engelmann, Theodora Kontogianni, Alexander Hermans, Jonas Schult and Bastian Leibe from RWTH Aachen University.

prediction example


This work is based on our paper Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds, which appeared at the IEEE International Conference on Computer Vision (ICCV) 2017, 3DRMS Workshop.

You can also check our project page for further details.

Deep learning approaches have made tremendous progress in the field of semantic segmentation over the past few years. However, most current approaches operate in the 2D image space. Direct semantic segmentation of unstructured 3D point clouds is still an open research problem. The recently proposed PointNet architecture presents an interesting step ahead in that it can operate on unstructured point clouds, achieving decent segmentation results. However, it subdivides the input points into a grid of blocks and processes each such block individually. In this paper, we investigate the question how such an architecture can be extended to incorporate larger-scale spatial context. We build upon PointNet and propose two extensions that enlarge the receptive field over the 3D scene. We evaluate the proposed strategies on challenging indoor and outdoor datasets and show improved results in both scenarios.

In this repository, we release code for training and testing various pointcloud semantic segmentation networks on arbitrary datasets.


If you find our work useful in your research, please consider citing:

  author    = {Francis Engelmann and
               Theodora Kontogianni and
               Alexander Hermans and
               Bastian Leibe},
  title     = {Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds},
  booktitle = {{IEEE} International Conference on Computer Vision, 3DRMS Workshop, {ICCV}},
  year      = {2017}


Install TensorFlow. The code has been tested with Python 3.6 and TensorFlow 1.8.


In order to get more representative blocks, it is encouraged to uniformly downsample the original point clouds. This is done via the following script:

python tools/ --data_dir path/to/dataset --cell_size 0.03

This statement will produce pointclouds where each point will be representative for its 3cm x 3cm x 3cm neighborhood.

To train/test a model for semantic segmentation on pointclouds, you need to run:

python --config path/to/config/file.yaml

Detailed instruction of the structure for the yaml config file can be found in the wiki. Additionally, some example configuration files are given in the folder experiments.

Note that the final evaluation is done on the full sized point clouds using k-nn interpolation.

Reproducing the scores of our paper for stanford indoor 3d

Downloading the data set

First of all, Stanford Large-Scale 3D Indoor Spaces Dataset has to be downloaded. Follow the instructions here. The aligned version 1.2 is used for our results.

Producing numpy files from the original dataset

Our pipeline cannot handle the original file type of s3dis. So, we need to convert it to npy files. Note that Area_5/hallway_6 has to be fixed manually due to format inconsistencies. The following script has to be run from the tools directory:

python --input_dir path/to/dataset --output_dir path/to/output

Downsampling for training

Before training, we downsampled the pointclouds.

python tools/ --data_dir path/to/dataset --cell_size 0.03

Training configuration scripts

Configuration files for all experiments are located in experiments/iccvw_paper_2017/*. For example, they can be launched as follows:

python --config experiments/iccvw_paper_2017/s3dis_mscu/s3dis_mscu_area_1.yaml

The above script will run our multi scale consolidation unit network on stanford indoor 3d with test area 1.

Evaluating on full scale point clouds

Reported scores on the dataset are based on the full scale pointclouds. In order to do so, we need to load the trained model and set the TEST flag.

Replace modus: TRAIN_VAL with

    modus: TEST
    model_path: 'path/to/trained/model/model_ckpts'

which is located in the log directory specified for training.

VKitti instructions

  • Coming soon...

Trained models for downloading

  • Coming soon...


Our code is released under MIT License (see LICENSE file for details).

You can’t perform that action at this time.