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Point2Graph: An End-to-end Point Cloud-based 3D Open-Vocabulary Scene Graph for Robot Navigation

This is the implementation of Object Detection and Classification module of the paper "Point2Graph: An End-to-end Point Cloud-based 3D Open-Vocabulary Scene Graph for Robot Navigation".

Authors: Yifan Xu, Ziming Luo, Qianwei Wang, Vineet Kamat, Carol Menassa

News:

[2025/02] Our paper is accepted by ICRA2025 🎉🎉🎉

Object Detection and Classification Pipeline

This module consists of two stages: (1) detection and localization using class-agnostic bounding boxes and DBSCAN filtering for object refinement, and (2) classification via cross-modal retrieval, connecting 3D point cloud data with textual descriptions, without requiring annotations or RGB-D alignment.

Pipeline Image

Getting Started

Installation

Step 1. Create a conda environment and activate it.

conda env create -f point2graph.yaml
conda activate point2graph

Step 2. install Minkowski Engine.

git clone https://github.com/NVIDIA/MinkowskiEngine.git
cd MinkowskiEngine
python setup.py install --blas_include_dirs=${CONDA_PREFIX}/include --blas=openblas

Step 3. install mmcv.

pip install openmim
mim install mmcv-full==1.6.1

Step 4. install third party support.

cd third_party/pointnet2/ && python setup.py install --user
cd ../..
cd utils && python cython_compile.py build_ext --inplace
cd ..

Dataset preparation

Scannet Data

  1. Download ScanNet v2 data HERE. Move/link the scans folder such that under scans there should be folders with names such as scene0001_01.
  2. Open the 'scannet' folder. Extract point clouds and annotations (semantic seg, instance seg etc.) by running python batch_load_scannet_data.py, which will create a folder named scannet_train_detection_data here.

Model preparation

You should

  • download the 3D Object Detection pre-trained model V-DETR, and put it in ./models/ folder.
  • download the 3D Object Classification pre-trained model Uni-3D and the clip model, and put them in ./Uni3D/downloads/ folder.

Testing

The test script is in the run.sh file. Once you have the datasets and model prepared, you can test this models as

bash run.sh

The script performs two functions:

  1. Get a set of point cloud of objects with unknown class and store them at ./results/objects/
  2. Retrieve and visualize the 3D object point cloud most relevant to the user's query

Acknowledgement

Point2Graph is built on the V-DETR, and Uni3D.

Citation

If you find this code useful in your research, please consider citing:

@misc{xu2024point2graphendtoendpointcloudbased,
      title={Point2Graph: An End-to-end Point Cloud-based 3D Open-Vocabulary Scene Graph for Robot Navigation}, 
      author={Yifan Xu and Ziming Luo and Qianwei Wang and Vineet Kamat and Carol Menassa},
      year={2024},
      eprint={2409.10350},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2409.10350}, 
}