Semantic segmentation in complex scenes not only relies on local object
appearance but also on object locations and the surrounding environment.
Nonetheless, it is difficult to model long-range context in the format of
pairwise point correlations due to its huge computational cost for large-scale
point clouds. In this paper, we propose to use regions as the intermediate
representation of point clouds instead of fine-grained points or voxels to
reduce the computational burden. We introduce a novel Region-Enhanced Feature
Learning network (REFL-Net) that leverages region correlations to enhance the
features of ambiguous points. We design a Region-based Feature Enhancement
module (RFE) which consists of a Semantic-Spatial Region Extraction (SSRE)
stage and a Region Dependency Modeling (RDM) stage. In the SSRE stage, we group
the input points into a set of regions according to the point distances in both
semantic and spatial space. In the RDM part, we explore region-wise semantic
and spatial relationships via a self-attention block on region features and
fuse point features with the region features to obtain more discriminative
representations. Our proposed RFE module is a plug-and-play module that can be
integrated with common semantic segmentation backbones. We conduct extensive
experiments on ScanNetv2 and S3DIS datasets, and evaluate our RFE module with
different segmentation backbones. Our REFL-Net achieves 1.8% mIoU gain on
ScanNetv2 and 1.0% mIoU gain on S3DIS respectively with negligible
computational cost compared to the backbone networks. Both quantitative and
qualitative results show the powerful long-range context modeling ability and
strong generalization ability of our REFL-Net.

本文提出了一种基于区域的特征强化网络 (REFL-Net)，将点云表示为区域来减少计算压力，并设计了以区域为基础的特征强化模块 (RFE) 来提高模糊点的特征表示，实现了强大的长程上下文建模和领域泛化能力。实验结果显示，REFL-Net 在 ScanNetv2 和 S3DIS 数据集上均达到了显著的增益。