# Amodal Completion and Size Constancy in Natural Scenes

arXiv: https://arxiv.org/abs/1509.08147 · HTML: https://mlsanity.com/paper/1509.08147/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2015.

## Built on by (most cited first, 21 of 21)

- [pix2gestalt: Amodal Segmentation by Synthesizing Wholes](https://mlsanity.com/paper/2401.14398/) (2024)
- [HoloPart: Generative 3D Part Amodal Segmentation](https://mlsanity.com/paper/2504.07943/) (2025)
- [Occlusion-Aware Video Object Inpainting](https://mlsanity.com/paper/2108.06765/) (2021)
- [Weakly Aligned Feature Fusion for Multimodal Object Detection](https://mlsanity.com/paper/2204.09848/) (2022)
- [Visiting the Invisible: Layer-by-Layer Completed Scene Decomposition](https://mlsanity.com/paper/2104.05367/) (2021)
- [Image Amodal Completion: A Survey](https://mlsanity.com/paper/2207.02062/) (2022)
- [HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation](https://mlsanity.com/paper/2504.13072/) (2025)
- [Object-level Scene Deocclusion](https://mlsanity.com/paper/2406.07706/) (2024)
- [AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving](https://mlsanity.com/paper/2309.06547/) (2023)
- [TACO: Taming Diffusion for in-the-wild Video Amodal Completion](https://mlsanity.com/paper/2503.12049/) (2025)
- [Beyond the Visible: Benchmarking Occlusion Perception in Multimodal Large Language Models](https://mlsanity.com/paper/2508.04059/) (2025)
- [2.5D Visual Relationship Detection](https://mlsanity.com/paper/2104.12727/) (2021)
- [Amodal Depth Anything: Amodal Depth Estimation in the Wild](https://mlsanity.com/paper/2412.02336/) (2024)
- [Mitigating Perspective Distortion-induced Shape Ambiguity in Image Crops](https://mlsanity.com/paper/2312.06594/) (2023)
- [Humans as Checkerboards: Calibrating Camera Motion Scale for World-Coordinate Human Mesh Recovery](https://mlsanity.com/paper/2407.00574/) (2024)
- [Monocular Multi-Layer Layout Estimation for Warehouse Racks](https://mlsanity.com/paper/2103.09174/) (2021)
- [AutoLay: Benchmarking amodal layout estimation for autonomous driving](https://mlsanity.com/paper/2108.09047/) (2021)
- [Stable Diffusion-Based Approach for Human De-Occlusion](https://mlsanity.com/paper/2508.12663/) (2025)
- [A solution to generalized learning from small training sets found in infants repeated visual experiences of individual objects](https://mlsanity.com/paper/2510.15060/) (2025)
- [PHAC: Promptable Human Amodal Completion](https://mlsanity.com/paper/2603.14741/) (2026)
- [HyperDAM: Hyperspectral Distractor-Aware Memory with Amodal Expansion for SAM 3 Tracking](https://mlsanity.com/paper/2609.34396/) (2026)

## Abstract (arXiv)

We consider the problem of enriching current object detection systems with veridical object sizes and relative depth estimates from a single image. There are several technical challenges to this, such as occlusions, lack of calibration data and the scale ambiguity between object size and distance. These have not been addressed in full generality in previous work. Here we propose to tackle these issues by building upon advances in object recognition and using recently created large-scale datasets. We first introduce the task of amodal bounding box completion, which aims to infer the the full extent of the object instances in the image. We then propose a probabilistic framework for learning category-specific object size distributions from available annotations and leverage these in conjunction with amodal completion to infer veridical sizes in novel images. Finally, we introduce a focal length prediction approach that exploits scene recognition to overcome inherent scaling ambiguities and we demonstrate qualitative results on challenging real-world scenes.