NeuRBF: A Neural Fields Representation with Adaptive Radial Basis
  Functions
We present a novel type of neural fields that uses general radial bases for
signal representation. State-of-the-art neural fields typically rely on
grid-based representations for storing local neural features and N-dimensional
linear kernels for interpolating features at continuous query points. The
spatial positions of their neural features are fixed on grid nodes and cannot
well adapt to target signals. Our method instead builds upon general radial
bases with flexible kernel position and shape, which have higher spatial
adaptivity and can more closely fit target signals. To further improve the
channel-wise capacity of radial basis functions, we propose to compose them
with multi-frequency sinusoid functions. This technique extends a radial basis
to multiple Fourier radial bases of different frequency bands without requiring
extra parameters, facilitating the representation of details. Moreover, by
marrying adaptive radial bases with grid-based ones, our hybrid combination
inherits both adaptivity and interpolation smoothness. We carefully designed
weighting schemes to let radial bases adapt to different types of signals
effectively. Our experiments on 2D image and 3D signed distance field
representation demonstrate the higher accuracy and compactness of our method
than prior arts. When applied to neural radiance field reconstruction, our
method achieves state-of-the-art rendering quality, with small model size and
comparable training speed.
6515269791aa56a7b10910c3
Zhang Chen
claimed_verified
2023-09-28T19:03:15.096Z
6515bec8c2aedaa7e6929991
/avatars/29fa0acad5de98550cffd18bd118b4fb.svg
Zhang Chen
user
ZhangChenv
6515269791aa56a7b10910c4
Zhong Li
claimed_verified
2023-09-28T19:03:18.766Z
64fa3909dc465697354572d2
/avatars/722fc3c3ce0229fc4243bcdcabb42ea5.svg
Zhong Li
user
lizhong323
6515269791aa56a7b10910c5
Liangchen Song
claimed_verified
2023-10-02T07:00:12.873Z
63d9ea2aaefaa7352587151c
/avatars/2f02ee1b2a0018c74ef1d47883782bd5.svg
Liangchen Song
user
lsongx
6515269791aa56a7b10910c6
Lele Chen
6515269791aa56a7b10910c7
Jingyi Yu
6515269791aa56a7b10910c8
Junsong Yuan
6515269791aa56a7b10910c9
Yi Xu
NeuRBF: A Neural Fields Representation with Adaptive Radial Basis
  Functions
We present a novel type of neural fields that uses general radial bases for
signal representation. State-of-the-art neural fields typically rely on
grid-based representations for storing local neural features and N-dimensional
linear kernels for interpolating features at continuous query points. The
spatial positions of their neural features are fixed on grid nodes and cannot
well adapt to target signals. Our method instead builds upon general radial
bases with flexible kernel position and shape, which have higher spatial
adaptivity and can more closely fit target signals. To further improve the
channel-wise capacity of radial basis functions, we propose to compose them
with multi-frequency sinusoid functions. This technique extends a radial basis
to multiple Fourier radial bases of different frequency bands without requiring
extra parameters, facilitating the representation of details. Moreover, by
marrying adaptive radial bases with grid-based ones, our hybrid combination
inherits both adaptivity and interpolation smoothness. We carefully designed
weighting schemes to let radial bases adapt to different types of signals
effectively. Our experiments on 2D image and 3D signed distance field
representation demonstrate the higher accuracy and compactness of our method
than prior arts. When applied to neural radiance field reconstruction, our
method achieves state-of-the-art rendering quality, with small model size and
comparable training speed.
A new type of neural fields using general radial bases with adaptive positions and shapes outperforms existing methods in signal representation, spatial adaptivity, and rendering quality.
neural fields radial bases grid-based representations N-dimensional linear kernels spatial adaptivity multi-frequency sinusoid functions Fourier radial bases hybrid combination rendering quality neural radiance field reconstruction