  Semantic segmentation of 3D meshes is an important problem for 3D scene
understanding. In this paper we revisit the classic multiview representation of
3D meshes and study several techniques that make them effective for 3D semantic
segmentation of meshes. Given a 3D mesh reconstructed from RGBD sensors, our
method effectively chooses different virtual views of the 3D mesh and renders
multiple 2D channels for training an effective 2D semantic segmentation model.
Features from multiple per view predictions are finally fused on 3D mesh
vertices to predict mesh semantic segmentation labels. Using the large scale
indoor 3D semantic segmentation benchmark of ScanNet, we show that our virtual
views enable more effective training of 2D semantic segmentation networks than
previous multiview approaches. When the 2D per pixel predictions are aggregated
on 3D surfaces, our virtual multiview fusion method is able to achieve
significantly better 3D semantic segmentation results compared to all prior
multiview approaches and competitive with recent 3D convolution approaches.
