  Existing learning-based surface reconstruction methods from point clouds are
still facing challenges in terms of scalability and preservation of details on
large-scale point clouds. In this paper, we propose the SSRNet, a novel
scalable learning-based method for surface reconstruction. The proposed SSRNet
constructs local geometry-aware features for octree vertices and designs a
scalable reconstruction pipeline, which not only greatly enhances the
predication accuracy of the relative position between the vertices and the
implicit surface facilitating the surface reconstruction quality, but also
allows dividing the point cloud and octree vertices and processing different
parts in parallel for superior scalability on large-scale point clouds with
millions of points. Moreover, SSRNet demonstrates outstanding generalization
capability and only needs several surface data for training, much less than
other learning-based reconstruction methods, which can effectively avoid
overfitting. The trained model of SSRNet on one dataset can be directly used on
other datasets with superior performance. Finally, the time consumption with
SSRNet on a large-scale point cloud is acceptable and competitive. To our
knowledge, the proposed SSRNet is the first to really bring a convincing
solution to the scalability issue of the learning-based surface reconstruction
methods, and is an important step to make learning-based methods competitive
with respect to geometry processing methods on real-world and challenging data.
Experiments show that our method achieves a breakthrough in scalability and
quality compared with state-of-the-art learning-based methods.
