# 3D Reconstruction of Simple Objects from A Single View Silhouette Image

arXiv: https://arxiv.org/abs/1701.04752 · HTML: https://mlsanity.com/paper/1701.04752/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2017.

## Built on by (most cited first, 2 of 2)

- [Inferring Implicit 3D Representations from Human Figures on Pictorial Maps](https://mlsanity.com/paper/2209.02385/) (2022)
- [GSGTrack: Gaussian Splatting-Guided Object Pose Tracking from RGB Videos](https://mlsanity.com/paper/2412.02267/) (2024)

## Abstract (arXiv)

While recent deep neural networks have achieved promising results for 3D reconstruction from a single-view image, these rely on the availability of RGB textures in images and extra information as supervision. In this work, we propose novel stacked hierarchical networks and an end to end training strategy to tackle a more challenging task for the first time, 3D reconstruction from a single-view 2D silhouette image. We demonstrate that our model is able to conduct 3D reconstruction from a single-view silhouette image both qualitatively and quantitatively. Evaluation is performed using Shapenet for the single-view reconstruction and results are presented in comparison with a single network, to highlight the improvements obtained with the proposed stacked networks and the end to end training strategy. Furthermore, 3D re- construction in forms of IoU is compared with the state of art 3D reconstruction from a single-view RGB image, and the proposed model achieves higher IoU than the state of art of reconstruction from a single view RGB image.