Context-based Visual Language Place Recognition

Download Dataset

• KITTI dataset
  
  • image_2 (.png) and ground truth poses (.txt) are required.
    
  • download link

Download Checkpoints

NetVLAD

• download link

LSeg

• download link

Folder Structure

${ROOT}
└── data/
     └── kitti/
          └── 00/
               └── image_2/
               └── poses.txt
     └── pittsburgh/             
└── netvlad/
     └── checkpoints/
└── lseg/
     └── codebook.npy
     └── text_embedding.npy
     └── sripts/
         └── checkpoints/
              └── demo_e200.ckpt

Evaluation

BoQ

Pittsburgh Dataset

• dataset: Dataset to use. (default: pittsburgh, options: pittsburgh, kitti)

python run_boq.py --dataset=pittsburgh --split=val

KITTI Dataset

• dataset: Dataset to use. (default: pittsburgh, options: pittsburgh, kitti)

python run_boq.py --dataset=kitti

NetVLAD

Pittsburgh Dataset

• mode: Select mode. (default: train, options: train, test, cluster)
• resume: Path to load checkpoint from, for resuming training or testing.
• dataset: Dataset to use. (default: pittsburgh, options: pittsburgh, kitti)
• random: Randomize dataset for test. (default: False)
• extract_dataset: Extract partial dataset from whole dataset. (default: False)

python run_netvlad.py --split=val --mode=test --resume=./netvlad --dataset=pittsburgh

KITTI Dataset

• Use image_2 for the test.
• mode: Select mode. (default: train, options: train, test, cluster)
• resume: Path to load checkpoint from, for resuming training or testing.
• dataset: Dataset to use. (default: pittsburgh, options: pittsburgh, kitti)
• random: Randomize dataset for test. (default: False)

python run_netvlad.py --split=val --mode=test --resume=./netvlad --dataset=kitti

DBoW

KITTI Dataset

python run_dbow.py

Our Method

Creat Text Embedding

• Input custom label set to create text embedding.

cd <path to repository>
python build_text_embedding.py

Pittsburgh Dataset

• data_path: Path to data. (default: ./data)
• dataset: Dataset to use. (default: pittsburgh, options: pittsburgh, kitti)
• random: Randomize dataset for test. (default: False)
• build_codebook: If True, generate codebook for BoW. If False calculate recall for query images. (default: False)
• use_codebook: If True, use predefined codebook. (default: False)
• extract_dataset: Extract partial dataset from whole dataset. (default: False)
• dynamic_objects: Index of dynamic objects within text embedding
• save_log: Save log messages (default: False)

cd <path to repository>
python run_vlpr.py --dataset=pittsburgh
# ex) python run_vlpr.py --dataset=pittsburgh --dynamic_objects 7 8 9 10 11 1 18 19 20 21 22 28

KITTI Dataset

• data_path: Path to data. (default: ./data)
• dataset: Dataset to use. (default: pittsburgh, options: pittsburgh, kitti)
• random: Randomize dataset for test. (default: False)
• build_codebook: If True, generate codebook for BoW. If False calculate recall for query images. (default: False)
• use_codebook: If True, use predefined codebook. (default: False)
• extract_dataset: Extract partial dataset from whole dataset. (default: False)
• dynamic_objects: Index of dynamic objects within text embedding
• save_log: Save log messages (default: False)

cd <path to repository>
python run_vlpr.py --dataset=kitti
# ex) python run_vlpr.py --dataset=kitti --dynamic_objects 7 8 9 10 11 12 18 19 20 21 22 28

Visualize Centroid of Cluster

• Visualization of KITTI 00 Sequence (000001)

• image_embedding_file: Path to image embedding file
• text_embedding_file: Path to text embedding file
• dynamic_objects: Index of dynamic objects within text embedding

python visualize_cluster_centroid.py
# ex) python visualize_cluster_centroid.py --dynamic_objects 7 8 9 10 11 12 18 19 20 21 22 28