Amodal Instance Segmentation with KINS Dataset

Amodal instance segmentation, a new direction of instance segmentation, aims to segment each object instance involving its invisible, occluded parts to imitate human ability. This task requires to reason objects' complex structure. Despite important and futuristic, this task lacks data with large-scale and detailed annotation, due to the difficulty of correctly and consistently labeling invisible parts, which creates the huge barrier to explore the frontier of visual recognition. In this paper, we augment KITTI with more instance pixel-level annotation for 8 categories, which we call KITTI INStance dataset (KINS). We propose the network structure to reason invisible parts via a new multi-task framework with Multi-Level Coding (MLC), which combines information in various recognition levels. Extensive experiments show that our MLC effectively improves both amodal and inmodal segmentation. The KINS dataset and our proposed method are made publicly available.