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Found 3 papers, 3 papers with code
Surface Reconstruction from Point Clouds by Learning Predictive Context Priors
To reconstruct a surface at a specific query location at inference time, these methods then match the local reconstruction target by searching for the best match in the local prior space (by optimizing the parameters encoding the local context) at the given query location.
Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors
Our key idea is to infer signed distances by pushing both the query projections to be on the surface and the projection distance to be the minimum.
Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces
Specifically, we train a neural network to pull query 3D locations to their closest points on the surface using the predicted signed distance values and the gradient at the query locations, both of which are computed by the network itself.
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