This paper introduces a deep architecture for segmenting 3D objects into
their labeled semantic parts. Our architecture combines image-based Fully
Convolutional Networks (FCNs) and surface-based Conditional Random Fields
(CRFs) to yield coherent segmentations of 3D shapes. The image-based FCNs are
used for efficient view-based reasoning about 3D object parts. Through a
special projection layer, FCN outputs are effectively aggregated across
multiple views and scales, then are projected onto the 3D object surfaces.
Finally, a surface-based CRF combines the projected outputs with geometric
consistency cues to yield coherent segmentations. The whole architecture
(multi-view FCNs and CRF) is trained end-to-end. Our approach significantly
outperforms the existing state-of-the-art methods in the currently largest
segmentation benchmark (ShapeNet). Finally, we demonstrate promising
segmentation results on noisy 3D shapes acquired from consumer-grade depth
cameras.

该论文提出了一种用于将 3D 物体分割成标记语义部件的深度架构。该架构结合了基于图像的完全卷积网络和基于表面的条件随机场，以产生 3D 形状的连贯分割。通过特殊的投影层，图像 FCN 输出 被有效聚合在多个视图和比例上，然后投影到 3D 物体表面上。最后，基于表面的 CRF 将投影输出与几何一致性线索结合起来，以产生连贯的分割。整个架构（多视图 FCN 和 CRF）可以端对端地训练。该方法在当前最大的分割基准（ShapeNet）中明显优于现有的最先进方法。最后，我们展示了从消费级深度相机获取的嘈杂 3D 形状有着可喜的分割结果。