  Learning-based 3D reconstruction using implicit neural representations has
shown promising progress not only at the object level but also in more
complicated scenes. In this paper, we propose Dynamic Plane Convolutional
Occupancy Networks, a novel implicit representation pushing further the quality
of 3D surface reconstruction. The input noisy point clouds are encoded into
per-point features that are projected onto multiple 2D dynamic planes. A
fully-connected network learns to predict plane parameters that best describe
the shapes of objects or scenes. To further exploit translational equivariance,
convolutional neural networks are applied to process the plane features. Our
method shows superior performance in surface reconstruction from unoriented
point clouds in ShapeNet as well as an indoor scene dataset. Moreover, we also
provide interesting observations on the distribution of learned dynamic planes.
