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Found 3 papers, 2 papers with code
Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsampling
At inference time, we randomly sample queries around the sparse point cloud, and project these query points onto the zero-level set of the learned implicit field to generate a dense point cloud.
Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection
We pull the non-zero level sets onto the zero level set with gradient constraints which align gradients over different level sets and correct unsigned distance errors on the zero level set, leading to a smoother and more continuous unsigned distance field.
NeAF: Learning Neural Angle Fields for Point Normal Estimation
To resolve these issues, we propose an implicit function to learn an angle field around the normal of each point in the spherical coordinate system, which is dubbed as Neural Angle Fields (NeAF).
Ranked #3 on
Surface Normals Estimation
on PCPNet
