* indicates equal contribution
We present DynaMoN, a motion aware fast and robust camera localization approach for novel view synthesis. DynaMoN can handle not only the motion of known objects using semantic segmentation masks but also that of unknown objects using a motion segmentation mask. Furthermore, it retrieves the camera poses faster and more robust compared to classical SfM approaches enabling a more accurate 4D scene representation. Compared to the state-of-the-art, DynaMoN outperforms other dynamic camera localization approaches and shows better results for novel view synthesis.
Dynamic reconstruction with neural radiance fields (NeRF) requires accurate camera poses. These are often hard to retrieve with existing structure-from-motion (SfM) pipelines as both camera and scene content can change. We propose DynaMoN that leverages simultaneous localization and mapping (SLAM) jointly with motion masking to handle dynamic scene content. Our robust SLAM-based tracking module significantly accelerates the training process of the dynamic NeRF while improving the quality of synthesized views at the same time. Extensive experimental validation on TUM RGB-D, BONN RGB-D Dynamic and the DyCheck’s iPhone dataset, three real-world datasets, shows the advantages of DynaMoN both for camera pose estimation and novel view synthesis.
DynaMoN enables better visual results in dynamic scenes.
@misc{schischka2024dynamon,
   title={DynaMoN: Motion-Aware Fast and Robust Camera Localization for Dynamic Neural
Radiance Fields},
   author={Nicolas Schischka and Hannah Schieber and Mert Asim Karaoglu and Melih Görgülü
and Florian Grötzner and Alexander Ladikos and Daniel Roth and Nassir Navab and Benjamin Busam},
   year={2024},
   eprint={2309.08927},
   archivePrefix={arXiv},
   primaryClass={cs.CV}
}