核心概念
The author introduces Unsigned Orthogonal Distance Fields (UODFs) as a novel neural implicit representation for accurate reconstruction of diverse 3D shapes. UODFs offer unique characteristics that differentiate them from traditional Signed Distance Fields (SDF) and Unsigned Distance Fields (UDF), leading to improved reconstruction accuracy.
摘要
The content discusses the introduction of UODFs as a new approach for neural implicit representation in 3D shape reconstruction. It highlights the limitations of SDF and UDF methods, explaining how UODFs address these issues by providing accurate surface point reconstruction without interpolation errors. The paper presents detailed experiments and comparisons with existing methods, showcasing the superior performance of UODFs in reconstructing watertight, non-watertight, and complex shapes.
統計資料
"We propose unsigned orthogonal distance fields (UODFs) based NIR."
"UODFs diverge from conventional SDF and UDF in their unique characteristics."
"Our method consistently outperforms MeshUDF at all tested grid resolutions."