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DynaMoN: Motion-Aware Fast And Robust Camera -Localization for Dynamic NeRF
+ Localization for Dynamic NeRF --
- arixv
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COLMAP + HexPlane
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Abstract
- - 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. + 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.