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We present LoD-NeuS, an efficient neural representation for high-frequency geometry detail recovery and anti-aliased novel view rendering.
Hf-neus: Improved surface reconstruction using high-frequency details
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HR-NeuS: Recovering High-Frequency Surface Geometry via Neural Implicit Surfaces
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PET-NeuS: Positional Encoding Triplanes for Neural Surfaces
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Local Implicit Ray Function for Generalizable Radiance Field Representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
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Pyramid NeRF: Frequency Guided Fast Radiance Field Optimization
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NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination
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