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Grounding and Enhancing Grid-based Models for Neural Fields
April 1, 2024, 4:44 a.m. | Zelin Zhao, Fenglei Fan, Wenlong Liao, Junchi Yan
cs.CV updates on arXiv.org arxiv.org
Abstract: Many contemporary studies utilize grid-based models for neural field representation, but a systematic analysis of grid-based models is still missing, hindering the improvement of those models. Therefore, this paper introduces a theoretical framework for grid-based models. This framework points out that these models' approximation and generalization behaviors are determined by grid tangent kernels (GTK), which are intrinsic properties of grid-based models. The proposed framework facilitates a consistent and systematic analysis of diverse grid-based models. Furthermore, …
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