JSPNet: Learning joint semantic & instance segmentation of point clouds via feature self-similarity and cross-task probability

Highlights：

• To our best knowledge, the common ground of mutual promotion and conflict in joint semantic and instance segmentation are firstly analyzed in detail in this work.

• The similarity-based feature fusion module could maintain discriminative features and characterize inconspicuous content.

• The cross-task probability-based feature fusion module could model the task-relatedness by establishing probabilistic correlation between semantic and instance features.

• The proposed method significantly outperforms state-of-the-arts in both semantic and instance segmentation on two benchmarks.