Abstract
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 simultaneouslysegment 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.
