Object Re-identification from Point Clouds

paper | dataset

Abstract

Object re-identification (ReID) from images plays a critical role in application domains of image retrieval (surveillance, retail analytics, etc.) and multi-object tracking (autonomous driving, robotics, etc.). However, systems that additionally or exclusively perceive the world from depth sensors are becoming more commonplace without any corresponding methods for object ReID. In this work, we fill the gap by providing the first large-scale study of object ReID from point clouds and establishing its performance relative to image ReID. To enable such a study, we create two large-scale ReID datasets with paired image and LiDAR observations and propose a lightweight matching head that can be concatenated to any set or sequence processing backbone (e.g., PointNet or ViT), creating a family of comparable object ReID networks for both modalities. Run in Siamese style, our proposed point cloud ReID networks can make thousands of pairwise comparisons in real-time ($10$ Hz). Our findings demonstrate that their performance increases with higher sensor resolution and approaches that of image ReID when observations are sufficiently dense. Our strongest network trained at the largest scale achieves ReID accuracy exceeding $90%$ for rigid objects and $85%$ for deformable objects (without any explicit skeleton normalization). To our knowledge, we are the first to study object re-identification from real point cloud observations. 86

Citation

If you have found our work useful, please consider citing:

@article{therien2024pcreid,
  author    = {Therien, Benjamin 
               and Huang, Chenjie 
               and Chow, Adrian
               and Czarnecki, Krzysztof},
  title     = {Object Re-identification from Point Clouds},
  journal   = {WACV},
  year      = {2024},
}

Usage

Downloading our nuscenes reid dataset

Run the following command to download our reid dataset:

mkdir data
cd data
mkdir lstk
cd lstk
wget wiselab.uwaterloo.ca/nuscenes-reid/compressed_datasets/nuscenes-reid-dataset.tar.gz
wget wiselab.uwaterloo.ca/nuscenes-reid/compressed_datasets/sample_token_to_num.pkl
wget wiselab.uwaterloo.ca/nuscenes-reid/compressed_datasets/instance_token_to_id.pkl
wget wiselab.uwaterloo.ca/nuscenes-reid/compressed_datasets/instance_to_keyframes.pkl
wget wiselab.uwaterloo.ca/nuscenes-reid/compressed_datasets/ds_name_to_scene_token.pkl
wget wiselab.uwaterloo.ca/nuscenes-reid/compressed_datasets/sample_to_keyframes.pkl

After downloading the dataset, extract it to the data directory:

pigz -dc nuscenes-reid-dataset.tar.gz | tar -xf - -C /path/to/data

ls data should now show the following:

lstk

ls data/lstk should now show the following:

ds_name_to_scene_token.pkl  instance_to_keyframes.pkl  sample_to_keyframes.pkl
instance_token_to_id.pkl    sample_token_to_num.pkl    sparse-trainval-det-both nuscenes-reid-dataset.tar.gz

Downloading our Waymo reid dataset

Due to licencing restrictions placed on the Waymo Open Dataset (WOD), we must validate each users agreement to the WOD terms before providing them access to the dataset. Please send me an email if you would like to get access to the dataset.

After downloading the dataset, extract the train and validations datasets to the data directory:

cd point-cloud-reid
cd data/lstk

NUM_THREADS=8
zstd -d --threads=$NUM_THREADS waymo_both_train.tar.zst -c | tar -xvf -
zstd -d --threads=$NUM_THREADS waymo_both_val.tar.zst -c | tar -xvf -

ls data should now show the following:

lstk

ls data/lstk should now show the following:

sparse-waymo-det-both-val  updated_sparse-waymo-det-both-train  waymo_both_train.tar.zst  waymo_both_val.tar.zst
waymo_infos_train_autolab.pkl waymo_infos_val_autolab.pkl waymo_instance_token_to_id_train.pkl waymo_instance_token_to_id_val.pkl

Downloading our pre-trained models

Run the following command to download our pre-trained models:

./tools/download_pretrained.sh

Environment Setup

We have always used docker containers during development. Therefore, we recommend you do the same.

docker pull benjamintherien/dev:bevfusion_base-pt3d-pyg-o3d-

After downloading our docker image, run the container. You can use the following python script, filling in your own values where needed:

python docker/run_docker.py

Outside of the container, at the same level as your clone of this repository clone the lamtk repository:

git clone https://github.com/c7huang/lamtk

Then, inside the container, run the following commands to install the lamtk package and other dependencies:

./tools/script_new_install.sh

Finally, during development and evaluation, we have always used neptune to log our runs. Based on our limited testing, diabling neptune logging causes errors that we have not yet debugged. Until we remove this dependency in the code you will need specify your neptune project and api Key in the following configuration files:

configs_reid/_base_/reidentification_runtime_testing.py
configs_reid/_base_/reidentification_runtime.py

Evaluation

We also provide instructions for evaluating our pretrained models. Assuming you have downloaded oour pre-trained models, you can run the following command to evaluate our models on the nuScenes reid dataset!

Here is a command to evaluate point-transformer trained for 4000 epochs, our strongest point model on nuScenes ReID:

CUDA_VISIBLE_DEVICES=0,1 MASTER_ADDR=localhost torchpack dist-run -v -np 2 python tools/train.py configs_reid/reid_nuscenes_pts/testing_pts_point-transformer_r_nus_det_500e.py --checkpoint pretrained/nuscenes/pts_point-transformer_r_nus_det_4000e.pth

The number of GPUs can be changed by changing the -np flag. For example, to run on a two GPUs, run the following command:

CUDA_VISIBLE_DEVICES=0,1,2,3 MASTER_ADDR=localhost torchpack dist-run -v -np 4 python tools/train.py configs_reid/reid_nuscenes_pts/testing_pts_point-transformer_r_nus_det_500e.py --checkpoint pretrained/nuscenes/pts_point-transformer_r_nus_det_4000e.pth

For simple evaluation of all models, one can use launching_testing.py, which is easily customizable to test any of our pre-trained models.

Training

We provide instructions to reproduce our trained models on nuScenes ReID. For instance, to train DGCNN on nuScenes ReID, run the following command:

CUDA_VISIBLE_DEVICES=0,1,2,3 MASTER_ADDR=localhost torchpack dist-run -v -np 4 python tools/train.py configs_reid/reid_nuscenes_pts/pts_dgcnn_point-cat_nus_det_4x256_500e.py --seed 66

To train DeIT base on nuScenes ReID, run the following command:

CUDA_VISIBLE_DEVICES=0,1,2,3 MASTER_ADDR=localhost torchpack dist-run -v -np 4 python tools/train.py configs_reid/reid_nuscenes_image/rgb_deit-base_point-cat_pt_nus_det_4x60_200e.py --seed 66

To train Point-transformer on Waymo ReID, run the following command:

CUDA_VISIBLE_DEVICES=0,1,2,3 MASTER_ADDR=localhost torchpack dist-run -v -np 4 python tools/train.py configs_reid/reid_waymo_pts/pts_point-transformer_point-cat_waymo_det_4x256_400e.py --seed 66

We provide training commands to reproduce the main models in our study in launching_training.py.

Acknowledgements

Our repository is based on lamtk, bevfusion, and mmdetection3d

Known issues

There is an error in the caption of Fig.3 in the published version of the paper. This error is corrected in the Arxiv version.

Citation

If you have found our work useful, please consider citing:

@article{therien2024pcreid,
  author    = {Therien, Benjamin 
               and Huang, Chenjie 
               and Chow, Adrian
               and Czarnecki, Krzysztof},
  title     = {Object Re-identification from Point Clouds},
  journal   = {WACV},
  year      = {2024},
}