  Feedforward fully convolutional neural networks currently dominate in
semantic segmentation of 3D point clouds. Despite their great success, they
suffer from the loss of local information at low-level layers, posing
significant challenges to accurate scene segmentation and precise object
boundary delineation. Prior works either address this issue by post-processing
or jointly learn object boundaries to implicitly improve feature encoding of
the networks. These approaches often require additional modules which are
difficult to integrate into the original architecture.
  To improve the segmentation near object boundaries, we propose a
boundary-aware feature propagation mechanism. This mechanism is achieved by
exploiting a multi-task learning framework that aims to explicitly guide the
boundaries to their original locations. With one shared encoder, our network
outputs (i) boundary localization, (ii) prediction of directions pointing to
the object's interior, and (iii) semantic segmentation, in three parallel
streams. The predicted boundaries and directions are fused to propagate the
learned features to refine the segmentation. We conduct extensive experiments
on the S3DIS and SensatUrban datasets against various baseline methods,
demonstrating that our proposed approach yields consistent improvements by
reducing boundary errors. Our code is available at
https://github.com/shenglandu/PushBoundary.
