This is the pytorch implementation of CVPR 2024 paper: Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation (IEVE).
Now the action space aligns with the paper reported (velocity control). If you have any other issues, feel free to contact us!
Inspired by the human behavior of “getting closer to confirm” when recognizing distant objects, we formulate the task of determining whether an object matches the one in the goal image as a sequential decision problem. In addition, we design a novel matching function that relies not only on the current observation and goal image but also on the Euclidean distance between the agent and the potential target. We categorize the targets into confirmed target, potential target, and no-target (exploration), and allow the agent to actively choose among these three targets.
- We use challenge-2023-branch of habitat-sim, please follow the instructions to complete installation
- Install habitat-lab: (make sure you install our modified version of habitat-lab)
pip install -e 3rdparty/habitat-lab
- Install pytorch according to your system configuration
- cd to the root directory, install requirements
pip install -r requirements.txt
- Install LightGlue, please follow the official guidance
- Install detectron2 according to your system configuration
- Follow the instructions in habitat-lab
- Move the dataset or create a symlink at
data
- Download all the pretrained models from google drive and place in pretrained models of root directory.
To test in the val set, run:
python main.py
- Check
agents\instance_exp.py
to see the Switch Policy - If you have any questions, feel free to open issues
@inproceedings{lei2024instance,
title={Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation},
author={Lei, Xiaohan and Wang, Min and Zhou, Wengang and Li, Li and Li, Houqiang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={16329--16339},
year={2024}
}