  Semantic segmentation in complex scenes relies not only on object appearance
but also on object location and the surrounding environment. Nonetheless, it is
difficult to model long-range context in the format of pairwise point
correlations due to the huge computational cost for large-scale point clouds.
In this paper, we propose using 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 point feature
learning. We design a region-based feature enhancement (RFE) module, which
consists of a Semantic-Spatial Region Extraction stage and a Region Dependency
Modeling stage. In the first stage, the input points are grouped into a set of
regions based on their semantic and spatial proximity. In the second stage, we
explore inter-region semantic and spatial relationships by employing a
self-attention block on region features and then fuse point features with the
region features to obtain more discriminative representations. Our proposed RFE
module is plug-and-play and 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 1.7% mIoU gain on S3DIS with
negligible computational cost compared with backbone models. Both quantitative
and qualitative results show the powerful long-range context modeling ability
and strong generalization ability of our REFL-Net.
