# 3D Shape Perception Integrates Intuitive Physics and Analysis-by-Synthesis

arXiv: https://arxiv.org/abs/2301.03711 · HTML: https://mlsanity.com/paper/2301.03711/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 61 references that are in the map.
- First posted on arXiv 9 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)
- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [Partial success in closing the gap between human and machine vision](https://mlsanity.com/paper/2106.07411/) (2021)
- [Efficient acquisition rules for model-based approximate Bayesian computation](https://mlsanity.com/paper/1704.00520/) (2017)
- [Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose](https://mlsanity.com/paper/2110.14213/) (2021)
- [Modeling human intuitions about liquid flow with particle-based simulation](https://mlsanity.com/paper/1809.01524/) (2018)

## Built on by (most cited first, 1 of 1)

- [How does the primate brain combine generative and discriminative computations in vision?](https://mlsanity.com/paper/2401.06005/) (2024)

## Abstract (arXiv)

Many surface cues support three-dimensional shape perception, but people can sometimes still see shape when these features are missing -- in extreme cases, even when an object is completely occluded, as when covered with a draped cloth. We propose a framework for 3D shape perception that explains perception in both typical and atypical cases as analysis-by-synthesis, or inference in a generative model of image formation: the model integrates intuitive physics to explain how shape can be inferred from deformations it causes to other objects, as in cloth-draping. Behavioral and computational studies comparing this account with several alternatives show that it best matches human observers in both accuracy and response times, and is the only model that correlates significantly with human performance on difficult discriminations. Our results suggest that bottom-up deep neural network models are not fully adequate accounts of human shape perception, and point to how machine vision systems might achieve more human-like robustness.