Unknown Object Segmentation from Stereo Images

Unknown Object Segmentation from Stereo Images Maximilian Durner*, Wout Boerdijk*, Martin Sundermeyer, Werner Friedl, Zoltan-Csaba Marton, and Rudolph Triebel. Accepted at IROS2021. paper, dataset

Overview

Abstract

Although instance-aware perception is a key prerequisite for many autonomous robotic applications, most of the methods only partially solve the problem by focusing solely on known object categories. However, for robots interacting in dynamic and cluttered environments, this is not realistic and severely limits the range of potential applications. Therefore, we propose a novel object instance segmentation approach that does not require any semantic or geometric information of the objects beforehand. In contrast to existing works, we do not explicitly use depth data as input, but rely on the insight that slight viewpoint changes, which for example are provided by stereo image pairs, are often sufficient to determine object boundaries and thus to segment objects. Focusing on the versatility of stereo sensors, we employ a transformer-based architecture that maps directly from the pair of input images to the object instances. This has the major advantage that instead of a noisy, and potentially incomplete depth map as an input, on which the segmentation is computed, we use the original image pair to infer the object instances and a dense depth map. In experiments in several different application domains, we show that our Instance Stereo Transformer (INSTR) algorithm outperforms current state-of-the-art methods that are based on depth maps. Training code and pretrained models are available at https://github.com/DLR-RM/instr.

Citation

If you find our work useful, please cite us:

@article{durner2021unknown,
    title={Unknown Object Segmentation from Stereo Images}, 
    author={Maximilian Durner and Wout Boerdijk and Martin Sundermeyer and Werner Friedl and Zoltan-Csaba Marton and Rudolph Triebel},
    year={2021},
    eprint={2103.06796},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Content Description

This repository contains code and a pre-trained model for the Instance Stereo Transformer (INSTR), and everything necessary to reproduce the main results presented in our paper. It also contains scripts to generate synthetic data, and a demo script.

Requirements: Hardware

For Training

Nvidia GPU with >= 11GB memory (or adjust the batch size). We trained on a Nvidia RTX 2080 Ti.

For Testing

Nvidia GPU with >= 3GB memory

Requirements: Software

It is reommended that you use a conda environment for installation. Run conda env create -f environment.yaml to install a conda environment named instr. Then, run conda activate instr to activate it.

Getting Started

Pre-trained Model

We provide a pre-trained model (link). With it you can

• reproduce the main results from the paper
• directly use it for a demo
• use it as an initial starting point for fine-tuning

Read the following for more information.

Train / Fine-tune INSTR

To train INSTR from scratch following the procedure in the paper first create the synthetic dataset (see the README). Otherwise, adapt the dataloader accordingly, if you want to fine-tune / train on another dataset. Adapt the paths in the config, expecially lines 9, 13 and 17. The config format follows YACS, so you can also easily modify options via the command line (see command below).

To start a training, run python train.py --config-file configs/config.yaml OPTIONAL_ARGS, where OPTIONAL_ARGS overwrite config settings. For example, python train.py --config-file configs/config.yaml MODEL.AUX_DECODER_LOSS False AXIAL_ATTENTION False will train without disparity loss and axial attention.

Running with the default config will store config, models and tensorboard visualization in ./output/instr_{time_stamp}.

For fine-tuning it is suggested to reduce the learning rate by a factor of 10, e.g. python train.py --config-file configs/config.yaml OPTIMIZER.LR 0.00001.

Evaluate INSTR on STIOS

STIOS is a table-top object dataset recorded by two stereo sensors with manual annotated instances for each frame (website, code utilities). To evaluate and reproduce the experiments in the paper (Tab. 2), download STIOS and the pretrained model. Extract the pretrained model in the project's root directory. Then, run python predict_stios.py --state-dict pretrained_instr/models/pretrained_model.pth --root ./../data/STIOS/ --rcvisard. This will generate mIoU and F1 scores for every scene.

Demo

Download the pretrained model and extract the contents here. Overwrite the camera class so that it returns a pair of stereo images (RGB, np.array, uint8) from your stereo camera. Then, run python demo.py for the default demo.

Run python demo.py --help or have a look at the predictor class for further information.

Changes made to the original code

• all files with the _ipa suffix have been modified to work with a different dataset which has following structure:

  ├──root-folder
  │     ├── converted_gt
  │     ├── synthetic
  │     │   ├── 1
  │     │   │   ├── 0_class_segmaps.png
  │     │   │   ├── 0_colors_0.png
  │     │   │   ├── 0_colors_1.png
  │     │   │   ├── 0_depth_0.png
  │     │   │   ├── 0_depth_1.png
  │     │   │   ├── 1_class_segmaps.png
  │     │   │   ├── 1_colors_0.png
  │     │   │   ├── 1_colors_1.png
  │     │   │   ├── 1_depth_0.png
  │     │   │   ├── 1_depth_1.png
  │     │   │   ├── .
  │     │   │   ├── .
  │     │   │   ├── .
  │     │   │   ├── scene.yaml
  │     │   ├── 2
  │     │   ├── .
  │     │   ├── .
  │     │   └── .
  │     ├── train
  │     │   ├── 1
  │     │   ├── 2
  │     │   ├── .
  │     │   ├── .
  │     │   └── .
  │     └── val
  │         ├── 10
  │         ├── 18
  │         ├── .
  │         ├── .
  │         ├── .
  │         └── config.yaml

  • the synthetic dataset is generated using the blenderproc module and used config in the val folder is the config from blenderproc
  • the converted_gt folder contains the ground truth segmentation masks for the synthetic dataset converted from an ipa_utils.py function, if the line to_image(gt) is uncommented in predict_ipa.py

• the ipa_utils.py file has been modified the most so here a short overview of the modified functions:

  • to_image function has been added to convert the ground truth segmentation masks to images
  • load_folder and load_data fitted to dataset
  • resize_squeeze function to resize the gt to needed size
  • to_image_pred generates prediction images in the dataset folders
  • data_cleaner function to remove the generated images from the dataset
  • segmap_to_gt function to convert the segmentation maps (rgb) to ground truth images (grayscale) and filtering objects to fit the detection capabilities of the model

WiP

• png data loader for trainig:
  • changes done:
    • load_train_data and load_train_folder functions in ipa_utils.py to load the png data and return a list with following shape:
      • dimension1: folder
      • dimension2: dataset in folder (left, right, depth, segmap), numerated in datatset
      • dimension3: images in dataset
    • fitted dataloader_ipa.py and training.py file to call functions
    • fitted dataloader to handle list instead of dictionary (partly)
  • current error:

      File ¨instr/data_io/data_loader_ipa.py", line 111, in __getitem__
      disp = self.depth_to_disp(extracted_data[2][0], baseline)
      KeyError: 2
    

  • approaches I can think of:
    • try to use dict instead of list
• png to hdf5 converter
  • written converter as data_to_hdf5.py to keep using ´factory method´ in training.py
  • current problem (paraphrased):
    • image in dataloade.py line 41 im=ttf.to_pil_image(im) expects 2/3 dimensions, gets 4