NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation. (arXiv:2206.05837v3 [cs.CV] UPDATED)

In visual computing, 3D geometry is represented in many different forms
including meshes, point clouds, voxel grids, level sets, and depth images. Each
representation is suited for different tasks thus making the transformation of
one representation into another (forward map) an important and common problem.
We propose Omnidirectional Distance Fields (ODFs), a new 3D shape
representation that encodes geometry by storing the depth to the object's
surface from any 3D position in any viewing direction. Since rays are the
fundamental …

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