  One of the main obstacles to 3D semantic segmentation is the significant
amount of endeavor required to generate expensive point-wise annotations for
fully supervised training. To alleviate manual efforts, we propose GIDSeg, a
novel approach that can simultaneously learn segmentation from sparse
annotations via reasoning global-regional structures and individual-vicinal
properties. GIDSeg depicts global- and individual- relation via a dynamic edge
convolution network coupled with a kernelized identity descriptor. The ensemble
effects are obtained by endowing a fine-grained receptive field to a
low-resolution voxelized map. In our GIDSeg, an adversarial learning module is
also designed to further enhance the conditional constraint of identity
descriptors within the joint feature distribution. Despite the apparent
simplicity, our proposed approach achieves superior performance over
state-of-the-art for inferencing 3D dense segmentation with only sparse
annotations. Particularly, with $5\%$ annotations of raw data, GIDSeg
outperforms other 3D segmentation methods.
