# Amodal Segmentation Based on Visible Region Segmentation and Shape Prior

arXiv: https://arxiv.org/abs/2012.05598 · HTML: https://mlsanity.com/paper/2012.05598/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 30 of 43)

- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [Segmenting Moving Objects via an Object-Centric Layered Representation](https://mlsanity.com/paper/2207.02206/) (2022)
- [Amodal Ground Truth and Completion in the Wild](https://mlsanity.com/paper/2312.17247/) (2023)
- [Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling](https://mlsanity.com/paper/2109.11103/) (2021)
- [Amodal Panoptic Segmentation](https://mlsanity.com/paper/2202.11542/) (2022)
- [AISFormer: Amodal Instance Segmentation with Transformer](https://mlsanity.com/paper/2210.06323/) (2022)
- [Coarse-to-Fine Amodal Segmentation with Shape Prior](https://mlsanity.com/paper/2308.16825/) (2023)
- [E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation](https://mlsanity.com/paper/2203.04074/) (2022)
- [Perceiving the Invisible: Proposal-Free Amodal Panoptic Segmentation](https://mlsanity.com/paper/2205.14637/) (2022)
- [Image Amodal Completion: A Survey](https://mlsanity.com/paper/2207.02062/) (2022)
- [DoNet: Deep De-overlapping Network for Cytology Instance Segmentation](https://mlsanity.com/paper/2303.14373/) (2023)
- [Object-level Scene Deocclusion](https://mlsanity.com/paper/2406.07706/) (2024)
- [Using Diffusion Priors for Video Amodal Segmentation](https://mlsanity.com/paper/2412.04623/) (2024)
- [Self-supervised Amodal Video Object Segmentation](https://mlsanity.com/paper/2210.12733/) (2022)
- [Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baseline](https://mlsanity.com/paper/2206.00527/) (2022)
- [Recurrent Generic Contour-based Instance Segmentation with Progressive Learning](https://mlsanity.com/paper/2301.08898/) (2023)
- [Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation](https://mlsanity.com/paper/2309.13248/) (2023)
- [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)
- [ShapeFormer: Shape Prior Visible-to-Amodal Transformer-based Amodal Instance Segmentation](https://mlsanity.com/paper/2403.11376/) (2024)
- [Amodal Instance Segmentation with Diffusion Shape Prior Estimation](https://mlsanity.com/paper/2409.18256/) (2024)
- [PLUG: Revisiting Amodal Segmentation with Foundation Model and Hierarchical Focus](https://mlsanity.com/paper/2405.16094/) (2024)
- [Unlocking Constraints: Source-Free Occlusion-Aware Seamless Segmentation](https://mlsanity.com/paper/2506.21198/) (2025)
- [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)
- [Referring Layer Decomposition](https://mlsanity.com/paper/2602.19358/) (2026)
- [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)
- [Amodal Intra-class Instance Segmentation: Synthetic Datasets and Benchmark](https://mlsanity.com/paper/2303.06596/) (2023)
- [Hyper-Transformer for Amodal Completion](https://mlsanity.com/paper/2405.19949/) (2024)

## Abstract (arXiv)

Almost all existing amodal segmentation methods make the inferences of occluded regions by using features corresponding to the whole image. This is against the human's amodal perception, where human uses the visible part and the shape prior knowledge of the target to infer the occluded region. To mimic the behavior of human and solve the ambiguity in the learning, we propose a framework, it firstly estimates a coarse visible mask and a coarse amodal mask. Then based on the coarse prediction, our model infers the amodal mask by concentrating on the visible region and utilizing the shape prior in the memory. In this way, features corresponding to background and occlusion can be suppressed for amodal mask estimation. Consequently, the amodal mask would not be affected by what the occlusion is given the same visible regions. The leverage of shape prior makes the amodal mask estimation more robust and reasonable. Our proposed model is evaluated on three datasets. Experiments show that our proposed model outperforms existing state-of-the-art methods. The visualization of shape prior indicates that the category-specific feature in the codebook has certain interpretability.