HC-INR uses a hierarchical hypernetwork to warp input coordinates into a disentangled space, raising the representable frequency bound while cutting parameters by 30-60% and boosting fidelity up to 4x over prior INRs.
Instant neural graphics primitives with a multiresolution hash encoding.ACM Transactions on Graphics (ToG), 41(4):1–15, 2022
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Scaling Implicit Fields via Hypernetwork-Driven Multiscale Coordinate Transformations
HC-INR uses a hierarchical hypernetwork to warp input coordinates into a disentangled space, raising the representable frequency bound while cutting parameters by 30-60% and boosting fidelity up to 4x over prior INRs.