Geometry-Informed Distance Candidate Selection for Adaptive Lightweight Omnidirectional Stereo Vision with Fisheye Images
<|reference_start|>Geometry-Informed Distance Candidate Selection for Adaptive Lightweight Omnidirectional Stereo Vision with Fisheye Images: Multi-view stereo omnidirectional distance estimation usually needs to build a cost volume with many hypothetical distance candidates. The cost volume building process is often computationally heavy considering the limited resources a mobile robot has. We propose a new geometry-informed way of distance candidates selection method which enables the use of a very small number of candidates and reduces the computational cost. We demonstrate the use of the geometry-informed candidates in a set of model variants. We find that by adjusting the candidates during robot deployment, our geometry-informed distance candidates also improve a pre-trained model's accuracy if the extrinsics or the number of cameras changes. Without any re-training or fine-tuning, our models outperform models trained with evenly distributed distance candidates. Models are also released as hardware-accelerated versions with a new dedicated large-scale dataset. The project page, code, and dataset can be found at https://theairlab.org/gicandidates/ .<|reference_end|>
@article{pulling2024geometry-informed,
    title={Geometry-Informed Distance Candidate Selection for Adaptive Lightweight
  Omnidirectional Stereo Vision with Fisheye Images},
    author={Conner Pulling, Je Hon Tan, Yaoyu Hu, Sebastian Scherer},
    journal={arXiv preprint arXiv:2405.05355},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05355},
    primaryClass={cs.CV cs.RO}
}