search

Yan-Xia / Text2Loc

The codes for CVPR2024 paper 'Text2Loc: 3D Point Cloud Localization from Natural Language'
50 stars 4 forks source link

readme

Text2Loc

This repository is the official implementation of our CVPR 2024 paper:

Text2Loc: 3D Point Cloud Localization from Natural Language

🔥🔥🔥 The project page is here.

Introduction

We focus on the relatively-understudied problem of point cloud localization from textual descriptions, to address the “last mile problem.” We introduce Text2Loc, a solution designed for city-scale position localization using textual descriptions. When provided with a point cloud representing the surroundings and a textual query describing a position, Text2Loc determines the most probable location of that described position within the map. The proposed Text2Loc achieves consistently better performance across all top retrieval numbers. Notably, it outperforms the best baseline by up to 2 times, localizing text queries below 5 m.

Installation

Create a conda environment and install basic dependencies:

git clone git@github.com:Yan-Xia/Text2Loc.git
cd Text2Loc

conda create -n text2loc python=3.10
conda activate text2loc

# Install the according versions of torch and torchvision
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch

# Install required dependencies
CC=/usr/bin/gcc-9 pip install -r requirements.txt

Datasets & Backbone

The KITTI360Pose dataset is used in our implementation.

For training and evaluation, we need cells and poses from Kitti360Pose dataset. The cells and poses folder can be downlowded from HERE

In addtion, to successfully implement prototype-based map cloning, we need to know the neighbors of each cell. We use direction folder to store the adjacent cells in different directions. The direction folder can be downloaded from HERE

If you want to train the model, you need to download the pretrained object backbone HERE:

The KITTI360Pose and the pretrained object backbone is provided by Text2Pos (paper, code)

The final directory structure should be:

│Text2Loc/
├──dataloading/
├──datapreparation/
├──data/
│   ├──k360_30-10_scG_pd10_pc4_spY_all/
│       ├──cells/
│           ├──2013_05_28_drive_0000_sync.pkl
│           ├──2013_05_28_drive_0002_sync.pkl
│           ├──...
│       ├──poses/
│           ├──2013_05_28_drive_0000_sync.pkl
│           ├──2013_05_28_drive_0002_sync.pkl
│           ├──...
│       ├──direction/
│           ├──2013_05_28_drive_0000_sync.json
│           ├──2013_05_28_drive_0002_sync.json
│           ├──...
├──checkpoints/
│   ├──pointnet_acc0.86_lr1_p256.pth
├──...

Load Pretrained Modules

We make our pre-trained models publicly available HERE. To run the evaluation, save them under

./checkpoints/coarse.pth
./checkpoints/fine.pth

Train

After setting up the dependencies and dataset, our models can be trained using the following commands:

Train Global Place Recognition (Coarse)

python -m training.coarse --batch_size 64 --coarse_embed_dim 256 --shuffle --base_path ./data/k360_30-10_scG_pd10_pc4_spY_all/   \
  --use_features "class"  "color"  "position"  "num" \
  --no_pc_augment \
  --fixed_embedding \
  --epochs 20 \
  --learning_rate 0.0005 \
  --lr_scheduler step \
  --lr_step 7 \
  --lr_gamma 0.4 \
  --temperature 0.1 \
  --ranking_loss contrastive \
  --hungging_model t5-large \
  --folder_name PATH_TO_COARSE

Train Fine Localization

python -m training.fine --batch_size 32 --fine_embed_dim 128 --shuffle --base_path ./data/k360_30-10_scG_pd10_pc4_spY_all/ \
  --use_features "class"  "color"  "position"  "num" \
  --no_pc_augment \
  --fixed_embedding \
  --epochs 35 \
  --learning_rate 0.0003 \
  --fixed_embedding \
  --hungging_model t5-large \
  --regressor_cell all \
  --pmc_prob 0.5 \
  --folder_name PATH_TO_FINE

Evaluation

Evaluation on Val Dataset

python -m evaluation.pipeline --base_path ./data/k360_30-10_scG_pd10_pc4_spY_all/ \
    --use_features "class"  "color"  "position"  "num" \
    --no_pc_augment \
    --no_pc_augment_fine \
    --hungging_model t5-large \
    --fixed_embedding \
    --path_coarse ./checkpoints/{PATH_TO_COARSE}/{COARSE_MODEL_NAME} \
    --path_fine ./checkpoints/{PATH_TO_FINE}/{FINE_MODEL_NAME}

Evaluation on Test Dataset

python -m evaluation.pipeline --base_path ./data/k360_30-10_scG_pd10_pc4_spY_all/ \
    --use_features "class"  "color"  "position"  "num" \
    --use_test_set \
    --no_pc_augment \
    --no_pc_augment_fine \
    --hungging_model t5-large \
    --fixed_embedding \
    --path_coarse ./checkpoints/{PATH_TO_COARSE}/{COARSE_MODEL_NAME} \
    --path_fine ./checkpoints/{PATH_TO_FINE}/{FINE_MODEL_NAME}

Citation

@InProceedings{xia2024text2loc,
      title={Text2Loc: 3D Point Cloud Localization from Natural Language},
      author={Xia, Yan and Shi, Letian and Ding, Zifeng and Henriques, Jo{\~a}o F and Cremers, Daniel},
      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      year={2024}
    }

To do list