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.

本研究提出了一种新型的动态平面卷积占据网络 (Dynamic Plane Convolutional Occupancy Networks)，它可以通过编码点云并将其投影到多个 2D 动态平面上，使用全连接网络来预测最佳的平面参数，进而从未定向的点云中实现更优秀的表面重建。该方法在 ShapeNet 和室内场景数据集上均表现出优越性能，并对学习到的动态平面的分布提供了有趣的观察结果。