Leveraging Neural Radiance Fields for Pose Estimation of an Unknown Space Object during Proximity Operations
<|reference_start|>Leveraging Neural Radiance Fields for Pose Estimation of an Unknown Space Object during Proximity Operations: We address the estimation of the 6D pose of an unknown target spacecraft relative to a monocular camera, a key step towards the autonomous rendezvous and proximity operations required by future Active Debris Removal missions. We present a novel method that enables an "off-the-shelf" spacecraft pose estimator, which is supposed to known the target CAD model, to be applied on an unknown target. Our method relies on an in-the wild NeRF, i.e., a Neural Radiance Field that employs learnable appearance embeddings to represent varying illumination conditions found in natural scenes. We train the NeRF model using a sparse collection of images that depict the target, and in turn generate a large dataset that is diverse both in terms of viewpoint and illumination. This dataset is then used to train the pose estimation network. We validate our method on the Hardware-In-the-Loop images of SPEED+ that emulate lighting conditions close to those encountered on orbit. We demonstrate that our method successfully enables the training of an off-the-shelf spacecraft pose estimation network from a sparse set of images. Furthermore, we show that a network trained using our method performs similarly to a model trained on synthetic images generated using the CAD model of the target.<|reference_end|>
@article{legrand2024leveraging,
    title={Leveraging Neural Radiance Fields for Pose Estimation of an Unknown
  Space Object during Proximity Operations},
    author={Antoine Legrand, Renaud Detry, Christophe De Vleeschouwer},
    journal={arXiv preprint arXiv:2405.12728},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12728},
    primaryClass={cs.CV eess.IV}
}