Reconstruction of man-made scenes from multi-view images is an important problem in computer vision and computer graphics. Observing that man-made scenes are usually composed of planar surfaces, we encode plane shape prior in reconstructing man-made scenes. Recent approaches for single-view reconstruction employ multi-branch neural networks to simultaneously segment planes and recover 3D plane parameters. However, the scale of available annotated data heavily limits the generalizability and accuracy of these supervised methods. In this paper, we propose multi-view regularization to enhance the capability of piecewise planar reconstruction during the training phase, without demanding extra annotated data. Our multi-view regularization enables the consistency among multiple views by making the feature embedding more robust against view change and lighting variations. Thus, the neural network trained by multi-view regularization performs better on a wide range of views and lightings in the test phase. Based on more consistent prediction results, we merge the recovered models from multiple views to reconstruct scenes. Our approach achieves state-of-the-art reconstruction performance compared to previous approaches on the public ScanNet dataset.

中文翻译：

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用多视图正则化重构分段平面场景

从多视图图像重建人造场景是计算机视觉和计算机图形学中的重要问题。观察到人造场景通常由平面组成，我们在重建人造场景之前先对平面形状进行编码。单视图重建的最新方法采用多分支神经网络来同时分割平面并恢复3D平面参数。但是，可用注释数据的规模极大地限制了这些受监督方法的通用性和准确性。在本文中，我们提出了多视图正则化以增强训练阶段的分段平面重建的能力，而无需额外的注释数据。我们的多视图正则化通过使功能嵌入针对视图变化和光照变化而更加强大，从而实现了多个视图之间的一致性。因此，在测试阶段，通过多视图正则化训练的神经网络在较大范围的视图和照明上表现更好。基于更一致的预测结果，我们合并了来自多个视图的恢复模型以重建场景。与公共ScanNet数据集上的先前方法相比，我们的方法可实现最新的重建性能。