  Segmentation of planar regions from a single RGB image is a particularly
important task in the perception of complex scenes. To utilize both visual and
geometric properties in images, recent approaches often formulate the problem
as a joint estimation of planar instances and dense depth through feature
fusion mechanisms and geometric constraint losses. Despite promising results,
these methods do not consider cross-task feature distillation and perform
poorly in boundary regions. To overcome these limitations, we propose X-PDNet,
a framework for the multitask learning of plane instance segmentation and depth
estimation with improvements in the following two aspects. Firstly, we
construct the cross-task distillation design which promotes early information
sharing between dual-tasks for specific task improvements. Secondly, we
highlight the current limitations of using the ground truth boundary to develop
boundary regression loss, and propose a novel method that exploits depth
information to support precise boundary region segmentation. Finally, we
manually annotate more than 3000 images from Stanford 2D-3D-Semantics dataset
and make available for evaluation of plane instance segmentation. Through the
experiments, our proposed methods prove the advantages, outperforming the
baseline with large improvement margins in the quantitative results on the
ScanNet and the Stanford 2D-3D-S dataset, demonstrating the effectiveness of
our proposals.
