i.e., neural implicit representations that map a 3D coordinate to a representation of whatever is...
Occupancy networks: Learning 3D reconstruction in function space DeepSDF: Learning continuous signed distance functions for shape representation NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis vsitzmann/awesome-implicit-representationsgithub.com/vsitzmann/awesome-implicit-representations ...
Specifically, we design neural spatial-spectral representation (NeSSR), which projects the deep features extracted from low-resolution inputs to the corresponding intensity values under target 3D coordinates (including 2D spatial positions and 1D spectral wavelengths). To achieve continuous reconstruction,...
However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal’s spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution ...
However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to...
3D scenes can be represented as 3D-structured neural scene representations, i.e., neural implicit representations that map a 3D coordinate to a representation of whatever is at that 3D coordinate. This then requires the formulation of a neural renderer, in particular, a ray-marcher, which perfor...
DINER: Disorder-Invariant Implicit Neural Representation∗ Shaowen Xie1,†, Hao Zhu1,†, Zhen Liu1,†, Qi Zhang2, You Zhou1, Xun Cao1, Zhan Ma1 1 School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China 2 AI Lab, Tencent Company, Shen...
COLLATOR: Consistent Spatial-Temporal Longitudinal Atlas Construction via Implicit Neural Representation - maopaom/COLLATOR
we further use implicit neural representations (INR) as the base architecture. Most INR architectures rely on sinusoidal positional encoding, which accounts for high-frequency information in data. However, the finite encoding size restricts the model’s representational power. Higher representational power...
As noticed by previous works [57, 72], directly inputting the spatial coordinates to an MLP of the implicit neural representation leads to a loss of high- frequency content. However, the high-frequency infor- mation, e.g. the edge information b...