mlsanityData to 7 Oct 2026

Image Amodal Completion: A Survey

arXiv 2207.02062 ↗First posted 5 Jul 2022cs.CV cs.LGcited by 18

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Abstract (arXiv)

Existing computer vision systems can compete with humans in understanding the visible parts of objects, but still fall far short of humans when it comes to depicting the invisible parts of partially occluded objects. Image amodal completion aims to equip computers with human-like amodal completion functions to understand an intact object despite it being partially occluded. The main purpose of this survey is to provide an intuitive understanding of the research hotspots, key technologies and future trends in the field of image amodal completion. Firstly, we present a comprehensive review of the latest literature in this emerging field, exploring three key tasks in image amodal completion, including amodal shape completion, amodal appearance completion, and order perception. Then we examine popular datasets related to image amodal completion along with their common data collection methods and evaluation metrics. Finally, we discuss real-world applications and future research directions for image amodal completion, facilitating the reader's understanding of the challenges of existing technologies and upcoming research trends.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 75 of its 104 references that are in the map. First posted on arXiv 5 Jul 2022.

Builds on

75 of 104 references.

References · 75

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 9; 2025: 4; 2026: 2.

18 papers.

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  1. 2024MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generationcited by 38
  2. 2024Open-World Amodal Appearance Completioncited by 18
  3. 2026PISCO: Precise Video Instance Insertion with Sparse Controlcited by 9
  4. 2023Amodal Optical Flowcited by 7
  5. 2024Occlusion-Aware Seamless Segmentationcited by 6
  6. 2023Occ2Net: Robust Image Matching Based on 3D Occupancy Estimation for Occluded Regionscited by 5
  7. 2024PLUG: Revisiting Amodal Segmentation with Foundation Model and Hierarchical Focuscited by 4
  8. 2024Amodal Depth Anything: Amodal Depth Estimation in the Wildcited by 4
  9. 2023SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop Scenescited by 1
  10. 2024DMAT: A Dynamic Mask-Aware Transformer for Human De-occlusioncited by 1
  11. 2024Foundation Models for Amodal Video Instance Segmentation in Automated Drivingcited by 1
  12. 2025GPT-4 for Occlusion Order Recoverycited by 1
6 more
  1. 2025Reasoning-Driven Amodal Completion: Collaborative Agents and Perceptual Evaluationcited by 1
  2. 2024Mask Guided Gated Convolution for Amodal Content Completioncited by 0
  3. 2024Track Anything Behind Everything: Zero-Shot Amodal Video Object Segmentationcited by 0
  4. 2025Bridging Perception and Language: A Systematic Benchmark for LVLMs' Understanding of Amodal Completion Reportscited by 0
  5. 2025A solution to generalized learning from small training sets found in infants repeated visual experiences of individual objectscited by 0
  6. 2026SemLayer: Semantic-aware Generative Segmentation and Layer Construction for Abstract Iconscited by 0