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.

注册森林环境的点云是精密林业局部激光雷达应用的必要先决条件。最先进的森林点云登记方法需要提取单个树属性，并且在处理具有致密树的真实森林点云时，它们具有效率的瓶颈。我们提出了一种自动，坚固，高效的方法，用于登记森林点云。我们的方法首先定位树从原料点云茎，然后根据他们的相对空间关系确定准变换茎匹配。相较于现有的方法，我们的算法不需要额外的单株属性，具有线性复杂的环境中的树木数量，允许它的大森林环境对齐点云。广泛的实验表明，我们的方法优于关于登记精度和稳健性的最先进的方法，并且在效率方面显着优于现有技术。此外，我们引入一个新的基准数据集，补充的开发和注册方法评价森林点云的极少数现有的开放的数据集。