Abstract:
Implicit Neural Representation of points has become an increasingly popular method
for representing 3D objects in a continuous function that maps any point in 3D space
to a feature vector. However, the representation has made it tough to capture high-
frequency details in the geometry of the objects. One way to improve the representation
of such shapes is by learning high-frequency details using Fourier Features. We demon-
strate the effectiveness of this approach by training a neural network to reconstruct
3D objects, using implicit neural representation and Fourier features. Our results show
that incorporating Fourier Features into Implicit Neural Representation improves the
accuracy and quality of reconstructed 3D objects.
