4 research outputs found
Surface Extraction from Neural Unsigned Distance Fields
We propose a method, named DualMesh-UDF, to extract a surface from unsigned
distance functions (UDFs), encoded by neural networks, or neural UDFs. Neural
UDFs are becoming increasingly popular for surface representation because of
their versatility in presenting surfaces with arbitrary topologies, as opposed
to the signed distance function that is limited to representing a closed
surface. However, the applications of neural UDFs are hindered by the notorious
difficulty in extracting the target surfaces they represent. Recent methods for
surface extraction from a neural UDF suffer from significant geometric errors
or topological artifacts due to two main difficulties: (1) A UDF does not
exhibit sign changes; and (2) A neural UDF typically has substantial
approximation errors. DualMesh-UDF addresses these two difficulties.
Specifically, given a neural UDF encoding a target surface to be
recovered, we first estimate the tangent planes of at a set of sample
points close to . Next, we organize these sample points into local
clusters, and for each local cluster, solve a linear least squares problem to
determine a final surface point. These surface points are then connected to
create the output mesh surface, which approximates the target surface. The
robust estimation of the tangent planes of the target surface and the
subsequent minimization problem constitute our core strategy, which contributes
to the favorable performance of DualMesh-UDF over other competing methods. To
efficiently implement this strategy, we employ an adaptive Octree. Within this
framework, we estimate the location of a surface point in each of the octree
cells identified as containing part of the target surface. Extensive
experiments show that our method outperforms existing methods in terms of
surface reconstruction quality while maintaining comparable computational
efficiency.Comment: ICCV 202