  Reconstructing 3D geometry from \emph{unoriented} point clouds can benefit
many downstream tasks. Recent shape modeling methods mostly adopt implicit
neural representation to fit a signed distance field (SDF) and optimize the
network by \emph{unsigned} supervision. However, these methods occasionally
have difficulty in finding the coarse shape for complicated objects, especially
suffering from the ``ghost'' surfaces (\ie, fake surfaces that should not
exist). To guide the network quickly fit the coarse shape, we propose to
utilize the signed supervision in regions that are obviously outside the object
and can be easily determined, resulting in our semi-signed supervision. To
better recover high-fidelity details, a novel importance sampling based on
tracked region losses and a progressive positional encoding (PE) prioritize the
optimization towards underfitting and complicated regions. Specifically, we
voxelize and partition the object space into \emph{sign-known} and
\emph{sign-uncertain} regions, in which different supervisions are applied.
Besides, we adaptively adjust the sampling rate of each voxel according to the
tracked reconstruction loss, so that the network can focus more on the
complicated under-fitting regions. To this end, we propose our semi-signed
prioritized (SSP) neural fitting, and conduct extensive experiments to
demonstrate that SSP achieves state-of-the-art performance on multiple datasets
including the ABC subset and various challenging data. The code will be
released upon the publication.

Computer Vision and Pattern Recognition