State of the Art in Surface Reconstruction from Point Clouds

403 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Reconstructing building mass models from UAV images (2016, background): “Most of traditional surface reconstruction techniques reconstruct objects with smooth surfaces by exploiting either increasingly sophisticated solvers or better formulation of prior knowledge [4].”
PCN: Point Completion Network (2018, methodology): “For example, surface reconstruction methods [3, 10, 33, 41, 49, 54, 62] generate smooth interpolations to ﬁll holes in locally incomplete scans.”
3D Skeletons: A State‐of‐the‐Art Report (2016, background): “This goal is closely related to surface reconstruction from unor-ganized point clouds, a well-studied problem in computer graphics [BTS ∗ 14].”
SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer (2021, background): “(1) Traditional point cloud completion methods [42, 1, 44, 26] usually assume a smooth surface of 3D shape, or utilize large-scaled complete shape dataset to infer the missing regions for incomplete shape.”
High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference (2017, background): “A detailed survey can be referred to [4].”