We achieve 3D semantic scene labeling by exploring semantic relation between
each point and its contextual neighbors through edges. Besides an
encoder-decoder branch for predicting point labels, we construct an edge branch
to hierarchically integrate point features and generate edge features. To
incorporate point features in the edge branch, we establish a hierarchical
graph framework, where the graph is initialized from a coarse layer and
gradually enriched along the point decoding process. For each edge in the final
graph, we predict a label to indicate the semantic consistency of the two
connected points to enhance point prediction. At different layers, edge
features are also fed into the corresponding point module to integrate
contextual information for message passing enhancement in local regions. The
two branches interact with each other and cooperate in segmentation. Decent
experimental results on several 3D semantic labeling datasets demonstrate the
effectiveness of our work.

本文介绍了一种利用边缘分支和层次化图框架实现点和其语境邻居之间的语义关系来实现 3D 语义场景标注的方法，并证明其有效性。

基于点边交互网络的点云语义分割

Hierarchical Point-Edge Interaction Network for Point Cloud Semantic  Segmentation

3D semantic scene labeling is fundamental to agents operating in the real
world. In particular, labeling raw 3D point sets from sensors provides
fine-grained semantics. Recent works leverage the capabilities of Neural
Networks (NNs), but are limited to coarse voxel predictions and do not
explicitly enforce global consistency. We present SEGCloud, an end-to-end
framework to obtain 3D point-level segmentation that combines the advantages of
NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields
(FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are
transferred back to the raw 3D points via trilinear interpolation. Then the
FC-CRF enforces global consistency and provides fine-grained semantics on the
points. We implement the latter as a differentiable Recurrent NN to allow joint
optimization. We evaluate the framework on two indoor and two outdoor 3D
datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance
comparable or superior to the state-of-the-art on all datasets.

本研究介绍了 SEGCloud，一种利用三线性插值和完全连接条件随机场等方式得到端到端三维点级分割的框架，可实现对室内和室外三维数据集进行准确的场景标注。