  We propose a novel 3D point cloud segmentation framework named SASO, which
jointly performs semantic and instance segmentation tasks. For semantic
segmentation task, inspired by the inherent correlation among objects in
spatial context, we propose a Multi-scale Semantic Association (MSA) module to
explore the constructive effects of the semantic context information. For
instance segmentation task, different from previous works that utilize
clustering only in inference procedure, we propose a Salient Point Clustering
Optimization (SPCO) module to introduce a clustering procedure into the
training process and impel the network focusing on points that are difficult to
be distinguished. In addition, because of the inherent structures of indoor
scenes, the imbalance problem of the category distribution is rarely considered
but severely limits the performance of 3D scene perception. To address this
issue, we introduce an adaptive Water Filling Sampling (WFS) algorithm to
balance the category distribution of training data. Extensive experiments
demonstrate that our method outperforms the state-of-the-art methods on
benchmark datasets in both semantic segmentation and instance segmentation
tasks.
