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Found 3 papers, 1 papers with code
Unsupervised Occupancy Learning from Sparse Point Cloud
Implicit Neural Representations have gained prominence as a powerful framework for capturing complex data modalities, encompassing a wide range from 3D shapes to images and audio.
Mixing-Denoising Generalizable Occupancy Networks
While current state-of-the-art generalizable implicit neural shape models rely on the inductive bias of convolutions, it is still not entirely clear how properties emerging from such biases are compatible with the task of 3D reconstruction from point cloud.
Ranked #1 on
3D Reconstruction
on ShapeNet
Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature Space
We explore a new idea for learning based shape reconstruction from a point cloud, based on the recently popularized implicit neural shape representations.
Ranked #3 on
3D Reconstruction
on ShapeNet
