NARF24: Estimating Articulated Object Structure for Implicit Rendering
<|reference_start|>NARF24: Estimating Articulated Object Structure for Implicit Rendering: Articulated objects and their representations pose a difficult problem for robots. These objects require not only representations of geometry and texture, but also of the various connections and joint parameters that make up each articulation. We propose a method that learns a common Neural Radiance Field (NeRF) representation across a small number of collected scenes. This representation is combined with a parts-based image segmentation to produce an implicit space part localization, from which the connectivity and joint parameters of the articulated object can be estimated, thus enabling configuration-conditioned rendering.<|reference_end|>
@article{lewis2024narf24:,
    title={NARF24: Estimating Articulated Object Structure for Implicit Rendering},
    author={Stanley Lewis, Tom Gao, and Odest Chadwicke Jenkins},
    journal={arXiv preprint arXiv:2409.09829},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09829},
    primaryClass={cs.RO cs.CV}
}