论文标题
动态平面卷积占用网络
Dynamic Plane Convolutional Occupancy Networks
论文作者
论文摘要
使用隐式神经表示的基于学习的3D重建不仅在对象级别上，而且在更复杂的场景中都表现出了有希望的进展。在本文中，我们提出了动态平面卷积占用网络，这是一种新颖的隐式表示，进一步推动了3D表面重建的质量。输入噪声点云被编码到投影到多个2D动态平面上的每点特征。完全连接的网络学会了预测最能描述对象或场景形状的平面参数。为了进一步利用翻译模棱两可，使用卷积神经网络来处理平面特征。我们的方法显示了Shapenet中未取向点云以及室内场景数据集的表面重建中的出色性能。此外，我们还提供了有关学到的动态平面分布的有趣观察。
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.
