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Discovering Object that Can Move

This is the repository for Discovering Object that Can Move, published at CVPR 2022.

[Project Page] [Paper] [Poster]

Update blogs

[July 2023]: [PD_bounding boxes]

1. Added 2D and 3D bounding boxes. The annotation format and labels are demonstrated in the readme.txt file.

[April 2023]: [PD_full]

1. Added depth and semantic masks.
2. Check our new work: Object Discovery from Motion-Guided Tokens; [code]

[Jan 2023]: [Simplified dataset and Additional data]

1. Change the download folder from box to google drive, which is easier to download.
2. Simplified TRI-PD (rgb, flow, and Ground-truth and estimated instance masks) dataset provided.
3. KITTI estimated masks and test data included.
4. Pre-trained models provided.
5. There are some problems with the raw data links currently, the simplified version is the correct one.

To download the files in google drive from a server, please check gdown. Some sample code to download the files in the folder:

import gdown
url = "https://drive.google.com/drive/folders/1q5AjqhoivJb67h9MZCgUtqb4CooDrZhC"

gdown.download_folder(url, quiet = False, use_cookies = False)

Set up

Python 3 dependencies:

• torch 1.7.1+CUDA11.0
• matplotlib
• cv2
• numpy
• scipy
• tqmd

The default training scipt takes ~15GB*4 for training

Running code

pre-processing

To run the model on the TRI-PD dataset, first download PD data and annotations and unzip the tar files. Then run the commend

python merge_pd.py --pd_dir /A/B --additional_dir /C/D

The full dataset will be merged to the path /A/B

The full dataset should be at the following structure:

root 
   - scene_000001
      - rgb
         - camera_01
            - 000000000000000005.png
            - ...
         - camera_04
         - camera_05
         - camera_06
         - camera_07
         - camera_08
         - camera_09
      - motion_vectors_2d
      - back_motion_vectors_2d
      - moving_masks
      - ari_masks
      - est_masks
   - scene_000003
   - ...

Train the model

python trianPD.py and python trainKITTI.py

See trainPD.sh and trainKITTI.sh for a sample training script

Evaluating the pre-trained model

To evaluate or infer on the test set, first download the pre-trained model (or train it with the training code), then run

python eval.py

to compute the FG.ARI score. We revised the code and fixed some bugs. The ARI score may be slightly different from the reported results in the main paper. See eval.sh for a sample evaluating script.

Visualize the slot masks

To infer and visualize on a video of arbitary length, see

PlotPD.py

for a sample code.

Pre-trained models

Pre-trained models are located in the pre-trained models folder in this drive.

TRI-PD Dataset

PD datasets:

Simplified dataset, full version of the dataset, and Additional annotations (moving object masks; dynamic object masks), test data, and pre-trained model: [Additional data]

Raw PD dataset (including RGB, semantic segmentation, instance segmentation, optical flow, depth, camera colibrations, 2D/3D bounding boxes, etc. ) is connected to TRI's Vidar project. Leave a message in the issues or contact zbao@andrew.cmu.edu for the annotations other than the provided ones.

Sample code to transfer the motion vectors to flow xy:

rgba = cv2.imread('x.png',-1)
r,g,b,a = rgba[:,:,0], rgba[:,:,1], rgba[:,:,2], rgba[:,:,3]
h,w,_ = flow.shape
dx_i = r+g*256
dy_i = b+a*256
flow_x = ((dx_i / 65535.0)*2.0-1.0) * w
flow_y = ((dy_i / 65535.0)*2.0 - 1.0) * h            

Acknowledgement

The slot attention modules is referred to the pytorch slot attention and the official google repo, the estimated motion segments are generated by Towards segmenting anything that moves repo.

Citation

@inproceedings{bao2022discovering,
    Author = {Bao, Zhipeng and Tokmakov, Pavel and Jabri, Allan and Wang, Yu-Xiong and Gaidon, Adrien and Hebert, Martial},
    Title = {Discorying Object that Can Move},
    Booktitle = {CVPR},
    Year = {2022},
}