Dec 6, 2022
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We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes. Instead of training a single model that combines all the frames, we formulate the dynamic modeling problem with an incremental learning paradigm in which per-frame model difference is trained to complement the adaption of a base model on the current frame. By exploiting the simple yet effective strategy of tuning with narrow bands, the proposed method achieves a feasible framework for handling on-the-fly video sequences with high training efficiency. The storage overhead induced by using explicit grid can be significantly reduced by using model difference based compression. We also introduce a curriculum learning strategy to further accelerate model optimization for each frame. Experiments on challenging video sequences demonstrate that our approach is capable of achieving a training speed of 10 seconds per-frame with competitive rendering quality, which attains 1500 × speedup over the state-of-the-art implicit methods.We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes. Instead of training a single model that combines all the frames, we formulate the dynamic modeling problem with an incremental learning paradigm in which per-frame model difference is trained to complement the adaption of a base model on the current frame. By exploiting the simple yet effective strategy of tuning with narrow bands, the proposed…
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