# Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation

arXiv: https://arxiv.org/abs/1905.12898 · HTML: https://mlsanity.com/paper/1905.12898/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 16 of 16)

- [Coarse-to-Fine Amodal Segmentation with Shape Prior](https://mlsanity.com/paper/2308.16825/) (2023)
- [Image Amodal Completion: A Survey](https://mlsanity.com/paper/2207.02062/) (2022)
- [Object-level Scene Deocclusion](https://mlsanity.com/paper/2406.07706/) (2024)
- [Self-supervised Amodal Video Object Segmentation](https://mlsanity.com/paper/2210.12733/) (2022)
- [BLADE: Box-Level Supervised Amodal Segmentation through Directed Expansion](https://mlsanity.com/paper/2401.01642/) (2024)
- [Occlusion-Aware Seamless Segmentation](https://mlsanity.com/paper/2407.02182/) (2024)
- [Image Translation as Diffusion Visual Programmers](https://mlsanity.com/paper/2401.09742/) (2024)
- [PLUG: Revisiting Amodal Segmentation with Foundation Model and Hierarchical Focus](https://mlsanity.com/paper/2405.16094/) (2024)
- [Amodal segmentation just like doing a jigsaw](https://mlsanity.com/paper/2107.07464/) (2021)
- [Unveiling the Invisible: Reasoning Complex Occlusions Amodally with AURA](https://mlsanity.com/paper/2503.10225/) (2025)
- [LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion](https://mlsanity.com/paper/2408.03238/) (2024)
- [Amodal SAM: A Unified Amodal Segmentation Framework with Generalization](https://mlsanity.com/paper/2604.20748/) (2026)
- [Shape Distribution Matters: Shape-specific Mixture-of-Experts for Amodal Segmentation under Diverse Occlusions](https://mlsanity.com/paper/2508.01664/) (2025)
- [Learnable Shape Prototypes with Occlusion-Geometry-Guided Injection for Amodal Instance Segmentation](https://mlsanity.com/paper/2605.24533/) (2026)
- [Amodal Segmentation for Laparoscopic Surgery Video Instruments](https://mlsanity.com/paper/2408.01067/) (2024)
- [Single Point, Full Mask: Velocity-Guided Level Set Evolution for End-to-End Amodal Segmentation](https://mlsanity.com/paper/2508.01661/) (2025)

## Abstract (arXiv)

In this work, we demonstrate yet another approach to tackle the amodal segmentation problem. Specifically, we first introduce a new representation, namely a semantics-aware distance map (sem-dist map), to serve as our target for amodal segmentation instead of the commonly used masks and heatmaps. The sem-dist map is a kind of level-set representation, of which the different regions of an object are placed into different levels on the map according to their visibility. It is a natural extension of masks and heatmaps, where modal, amodal segmentation, as well as depth order information, are all well-described. Then we also introduce a novel convolutional neural network (CNN) architecture, which we refer to as semantic layering network, to estimate sem-dist maps layer by layer, from the global-level to the instance-level, for all objects in an image. Extensive experiments on the COCOA and D2SA datasets have demonstrated that our framework can predict amodal segmentation, occlusion and depth order with state-of-the-art performance.