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We introduce a new neural signal model designed for efficient high-resolution representation of large-scale signals.
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Chabra, R., Lenssen, J.E., Ilg, E., Schmidt, T., Straub, J., Lovegrove, S., Newcombe, R.: Deep local shapes: Learning local sdf priors for detailed 3d reconstruction. In: IEEE European Conf. Computer Vision (ECCV). pp. 608–625. Springer (2020)
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Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. In: IEEE European Conf. Computer Vision (ECCV) (2020)
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Tancik, M., Srinivasan, P.P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J.T., Ng, R.: Fourier features let networks learn high frequency functions in low dimensional domains. Adv. Neural Info. Processing Systems (2020)
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