CBFLNet: Cross-boundary feature learning for large-scale point cloud segmentation

Abstract

Large-scale point cloud semantic segmentation presents a crucial yet challenging task. Current point cloud analysis approaches typically partition data into volumetric blocks, independently assigning labels to each point within these blocks. However, this strategy often compromises segmentation accuracy at block boundaries due to the isolated processing of each block, thus hindering the model’s contextual understanding. To address this issue, we present CBFLNet, an innovative semantic segmentation model that enables offset-free feature upsampling and cross-boundary feature learning. CBFLNet facilitates interaction between adjacent blocks, extending its receptive field beyond the input block. As a result, it notably mitigates errors in block boundary segmentation. CBFLNet is composed of three modules: an explicit local representation module, a symmetric sampling module, and a cross-boundary feature fusion module. The explicit local representation module is a plug-and-play module that takes two overlapping point cloud blocks as input to construct an approximate local spatial representation. In the symmetric sampling module, point features are symmetrically downsampled and upsampled, effectively avoiding feature offset caused by interpolation. Lastly, the cross-boundary feature fusion module enables cross-boundary local feature learning and multi-scale feature fusion. CBFLNet demonstrates a significant performance improvement, achieving a 0.9% mIoU increase over state-of-the-art methods on the S3DIS dataset and exhibiting competitive performance on the ScannetV2 dataset.