HOV-SG

This repository is the official implementation of the paper:

Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation

[Abdelrhman Werby]()*, Chenguang Huang*, Martin Büchner*, Abhinav Valada, and Wolfram Burgard.
*Equal contribution.

arXiv preprint arXiv:2403.17846, 2024
(Accepted for Robotics: Science and Systems (RSS), Delft, Netherlands, 2024.)

📰 Major Updates

• [29 Aug 2024] We added hm3dsem_walks dataset generation and hierarchical scene graph evaluation code.
  Please review the updated code structure and newly added dependencies for dataset construction.
  
  
• [01 Jul 2024] Initial release of HOV-SG including mapping and graph construction engine.

🏗 Setup

1. Clone and set up the HOV-SG repository

   
   git clone https://github.com/hovsg/HOV-SG.git
   cd HOV-SG

set up virtual environment and install habitat-sim afterwards separately to avoid errors.

conda env create -f environment.yaml conda activate hovsg conda install habitat-sim -c conda-forge -c aihabitat

set up the HOV-SG python package

pip install -e .

### OpenCLIP
HOV-SG uses the Open CLIP model to extract features from RGB-D frames. To download the Open CLIP model checkpoint `CLIP-ViT-H-14-laion2B-s32B-b79K` please refer to [Open CLIP](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K).
```bash
mkdir checkpoints
wget https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K/resolve/main/open_clip_pytorch_model.bin?download=true -O checkpoints/temp_open_clip_pytorch_model.bin && mv checkpoints/temp_open_clip_pytorch_model.bin checkpoints/laion2b_s32b_b79k.bin

Another option is to use the OVSeg fine-tuned Open CLIP model, which is available under here:

pip install gdown
gdown --fuzzy https://drive.google.com/file/d/17C9ACGcN7Rk4UT4pYD_7hn3ytTa3pFb5/view -O checkpoints/ovseg_clip.pth

SAM

HOV-SG uses SAM to generate class-agnostic masks for the RGB-D frames. To download the SAM model checkpoint sam_v2 execute the following:

wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth -O checkpoints/sam_vit_h_4b8939.pth

🖼️ Dataset Preparation

Habitat Matterport 3D Semantics

HOV-SG takes posed RGB-D sequences as input. In order to produce hierarchical multi-story scenes we make use of the Habitat 3D Semantics dataset (HM3DSem).

• Download the Habitat Matterport 3D Semantics dataset.

  Make sure that the raw HM3D dataset has the following structure:

  ``` ├── hm3d │ ├── hm3d_annotated_basis.scene_dataset_config.json # this file is necessary │ ├── val │ │ └── 00824-Dd4bFSTQ8gi │ │ ├── Dd4bFSTQ8gi.basis.glb │ │ ├── Dd4bFSTQ8gi.basis.navmesh │ │ ├── Dd4bFSTQ8gi.glb │ │ ├── Dd4bFSTQ8gi.semantic.glb │ │ └── Dd4bFSTQ8gi.semantic.txt ... ... ... ```

  We used the following scenes from the Habitat Matterport 3D Semantics dataset in our evaluation:

  Show Scenes ID

  1. `00824-Dd4bFSTQ8gi` 2. `00829-QaLdnwvtxbs` 3. `00843-DYehNKdT76V` 4. `00861-GLAQ4DNUx5U` 5. `00862-LT9Jq6dN3Ea` 6. `00873-bxsVRursffK` 7. `00877-4ok3usBNeis` 8. `00890-6s7QHgap2fW`

1. Our method requires posed input data. Because of that, we recorded trajectories for each sequence we evaluate on. We provide a script (hovsg/data/hm3dsem/gen_hm3dsem_walks_from_poses.py) that turns a set of camera poses (hovsg/data/hm3dsem/metadata/poses) into a sequence of RGB-D observations using the habitat-sim simulator. The output includes RGB, depth, poses and frame-wise semantic/panoptic ground truth:

   python data/habitat/gen_hm3dsem_from_poses.py --dataset_dir <hm3dsem_dir> --save_dir data/hm3dsem_walks/

2. Secondly, we construct a new hierarchical graph-structured dataset that is called hm3dsem_walks that includes ground truth based on all observations recorded. To produce this ground-truth data please execute the following: First, define the following config paths: main.package_path, main.dataset_path, main.raw_data_path, and main.save_path under config/create_graph.yaml. For each scene, define the main.scene_id, main.split. Next, execute the following to obtain floor-, region-, and object-level ground truth data per scene. We utilize every recorded frame without skipping (see parameter dataset.hm3dsem.gt_skip_frames) and recommend 128 GB of RAM to compile this as the scenes differ in size:

   cd HOV-SG
   python hovsg/data/hm3dsem/create_hm3dsem_walks_gt.py

To evaluate semantic segmentation cababilities, we used ScanNet and Replica.

ScanNet

To get an RGBD sequence for ScanNet, download the ScanNet dataset from the official website. The dataset contains RGB-D frames compressed as .sens files. To extract the frames, use the SensReader/python. We used the following scenes from the ScanNet dataset:

Replica

To get an RGBD sequence for Replica, Instead of the original Replica dataset, download the scanned RGB-D trajectories of the Replica dataset provided by Nice-SLAM. It contains rendered trajectories using the mesh models provided by the original Replica datasets. Download the Replica RGB-D scan dataset using the downloading script in Nice-SLAM.

wget https://cvg-data.inf.ethz.ch/nice-slam/data/Replica.zip -O data/Replica.zip && unzip data/Replica.zip -d data/Replica_RGBD && rm data/Replica.zip 

To evaluate against the ground truth semantics labels, you also need also to download the original Replica dataset from the Replica as it contains the ground truth semantics labels as .ply files.

git clone https://github.com/facebookresearch/Replica-Dataset.git data/Replica-Dataset
chmod +x data/Replica-Dataset/download.sh && data/Replica-Dataset/download.sh data/Replica_original

We only used the following scenes from the Replica dataset:

📂 Datasets file strutcre

The Data folder should have the following structure:

:rocket: Run

Create scene graphs (only for Habitat Matterport 3D Semantics):

python application/create_graph.py main.dataset=hm3dsem main.dataset_path=data/hm3dsem_walks/val/00824-Dd4bFSTQ8gi/ main.save_path=data/scene_graphs/00824-Dd4bFSTQ8gi

Visualize scene graph

python application/visualize_graph.py graph_path=data/scene_graphs/hm3dsem/00824-Dd4bFSTQ8gi/graph

Interactive visualization of scene graphs and natural language queries

Setup OpenAI

In order to test graph queries with HOV-SG, you need to setup an OpenAI API account with the following steps:

1. Sign up an OpenAI account, login your account, and bind your account with at least one payment method.
2. Get you OpenAI API keys, copy it.
3. Open your ~/.bashrc file, paste a new line export OPENAI_KEY=<your copied key>, save the file, and source it with command source ~/.bashrc. Another way would be to run export OPENAI_KEY=<your copied key> in the teminal where you want to run the query code.

Evaluate query against pre-built hierarchical scene graph

python application/visualize_query_graph.py main.graph_path=data/scene_graphs/hm3dsem/00824-Dd4bFSTQ8gi/graph

After launching the code, you will be asked to input the hierarchical query. An example is chair in the living room on floor 0. You can see the visualization of the top 5 target objects and the room it lies in.

Extract feature map for semantic segmentation (only ScanNet and Replica)

python application/semantic_segmentation.py main.dataset=replica main.dataset_path=Replica/office0 main.save_path=data/sem_seg/office0

Evaluate semantic segmentation (only ScanNet and Replica)

python application/eval/evaluate_sem_seg.py dataset=replica scene_name=office0 feature_map_path=data/sem_seg/office0

Evaluate predicted scene graphs (only Habitat 3D Semantics)

• Define the scene identifiers and paths of ground truth and the predicted scene graph in the config/eval_graph.yaml.
• Run the graph evaluation method:

  python application/eval/evaluate_graph.py 

📔 Abstract

Recent open-vocabulary robot mapping methods enrich dense geometric maps with pre-trained visual-language features. While these maps allow for the prediction of point-wise saliency maps when queried for a certain language concept, largescale environments and abstract queries beyond the object level still pose a considerable hurdle, ultimately limiting languagegrounded robotic navigation. In this work, we present HOVSG, a hierarchical open-vocabulary 3D scene graph mapping approach for language-grounded indoor robot navigation. Leveraging open-vocabulary vision foundation models, we first obtain state-of-the-art open-vocabulary segment-level maps in 3D and subsequently construct a 3D scene graph hierarchy consisting of floor, room, and object concepts, each enriched with openvocabulary features. Our approach is able to represent multistory buildings and allows robotic traversal of those using a cross-floor Voronoi graph. HOV-SG is evaluated on three distinct datasets and surpasses previous baselines in open-vocabulary semantic accuracy on the object, room, and floor level while producing a 75% reduction in representation size compared to dense open-vocabulary maps. In order to prove the efficacy and generalization capabilities of HOV-SG, we showcase successful long-horizon language-conditioned robot navigation within realworld multi-story environments.

If you find our work useful, please consider citing our paper:

@article{werby23hovsg,
Author = {Abdelrhman Werby and Chenguang Huang and Martin Büchner and Abhinav Valada and Wolfram Burgard},
Title = {Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation},
Year = {2024},
journal = {Robotics: Science and Systems},
} 

👩‍⚖️ License

For academic usage, the code is released under the MIT license. For any commercial purpose, please contact the authors.

🙏 Acknowledgment

This work was funded by the German Research Foundation (DFG) Emmy Noether Program grant number 468878300, the BrainLinks-BrainTools Center of the University of Freiburg, and an academic grant from NVIDIA.