Instance Segmentation for urban scene building

Requirements

Python 3.6.0
Pytorch 1.2.0
CUDA 10.0

Virtual Environment

conda create -n 3DBuildingInstanceSeg python==3.6
source activate 3DBuildingInstanceSeg

Install 3DBuildingInstanceSeg

(1) Clone from the repository.

git clone https://github.com/fullcyxuc/3DBuildingInstanceSeg.git
cd 3DBuildingInstanceSeg

(2) Install the dependent libraries.

pip install -r requirements.txt
conda install -c bioconda google-sparsehash

(3) For the SparseConv, we apply the implementation of spconv as Pointgroup did. The repository is recursively downloaded at step (1). We use the version 1.0 of spconv.

Note: it was modify spconv\spconv\functional.py to make grad_output contiguous. Make sure you use the modified spconv.

First please download the spconv, and put it into lib directory
To compile spconv, firstly install the dependent libraries.

conda install libboost
conda install -c daleydeng gcc-5 # need gcc-5.4 for sparseconv

Add the $INCLUDE_PATH$ that contains boost in lib/spconv/CMakeLists.txt. (Not necessary if it could be found.)

include_directories($INCLUDE_PATH$)

Compile the spconv library.

cd lib/spconv
python setup.py bdist_wheel

Run cd dist and use pip to install the generated .whl file.

(4) We also use other cuda and cpp extension(pointgroup_ops,pcdet_ops), and put them into the lib, to compile them:

cd lib/**  # (** refer to a specific extension)
python setup.py develop

Acknowledgement

This repo is built upon several repos, e.g., Pointgroup, SparseConvNet, spconv, IA-SSD and STPLS3D.