Scene-Recognition-in-3D(IROS'20)

This repository contains the source code and pretrained models from the paper Scene Recognition in 3D.

Indoor Scene Recognition in 3D

Shengyu Huang, Mikhail Usvyatsov, Konrad Schindler

To the best of our knowledge, we are the first to study the task of indoor scene recognition in 3D.

News

2020-11-26 Updates on pretrained weights and a small data pre-processing script
2019-02-28 initial release

Citation

If you find our work useful, please consider citing

@article{huang2020indoor,
  title={Indoor Scene Recognition in 3D},
  author={Huang, Shengyu and Usvyatsov, Mikhail and Schindler, Konrad},
  journal={IROS},
  year={2020}
}

Dependencies

The required libraries can be easily installed by runing

pip3 install -r requirements.txt

We use MinkowskiEngine(v0.4.2) as our 3D sparse convolution framework. If you have problem with compiling it, please refer to MinkowskiEngine for more details.

Data

We evaluate our model on ScanNet benchmark, the dataset is released under the ScanNet Term of Use, please contact ScanNet team for access.

We preprocess the raw data to be pth file for efficient access. We use torch_cluster for GPU-based effficient farthest point sampling, you can find a sample under the folder `tmp. The train/val/test split can be found under the folder split.


Pretrained model
You can download the pretrained models for testing from here.
Train and test
DGCNN
Please change

basetrain, baseval

 to your data folder. Then run 
<pre><code class="lang-shell">python3 main.py</code></pre>
PointNet++
Please change

basetrain, baseval

 to your data folder. Then run 
<pre><code class="lang-shell">python3 sceneclassification.py --addcolor True --num_points 4096</code></pre>
sparseConv

For Resnet14, change pathtrain and pathval` then run

python3 main.py --numpoints 4096 --usecolor True

Follow the following 3 steps to train the multi-task learner, these three parts only differ slightly in our implementation, please refer to three folders respectively for more details: