  Neural implicit functions have achieved impressive results for reconstructing
3D shapes from single images. However, the image features for describing 3D
point samplings of implicit functions are less effective when significant
variations of occlusions, views, and appearances exist from the image. To
better encode image features, we study a geometry-aware convolutional kernel to
leverage geometric relationships of point samplings by the proposed
\emph{spatial pattern}, i.e., a structured point set. Specifically, the kernel
operates at 2D projections of 3D points from the spatial pattern. Supported by
the spatial pattern, the 2D kernel encodes geometric information that is
crucial for 3D reconstruction tasks, while traditional ones mainly consider
appearance information. Furthermore, to enable the network to discover more
adaptive spatial patterns for further capturing non-local contextual
information, the kernel is devised to be deformable manipulated by a spatial
pattern generator. Experimental results on both synthetic and real datasets
demonstrate the superiority of the proposed method. Pre-trained models, codes,
and data are available at https://github.com/yixin26/SVR-SP.
