# One-stage Shape Instantiation from a Single 2D Image to 3D Point Cloud

arXiv: https://arxiv.org/abs/1907.10763 · HTML: https://mlsanity.com/paper/1907.10763/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 9 of 9)

- [Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images](https://mlsanity.com/paper/2307.08535/) (2023)
- [4D Myocardium Reconstruction with Decoupled Motion and Shape Model](https://mlsanity.com/paper/2308.14083/) (2023)
- [3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image](https://mlsanity.com/paper/2107.11010/) (2021)
- [A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction](https://mlsanity.com/paper/2107.09923/) (2021)
- [Modeling 3D cardiac contraction and relaxation with point cloud deformation networks](https://mlsanity.com/paper/2307.10927/) (2023)
- [3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks](https://mlsanity.com/paper/2307.07298/) (2023)
- [From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction](https://mlsanity.com/paper/2505.03599/) (2025)
- [Multi-objective point cloud autoencoders for explainable myocardial infarction prediction](https://mlsanity.com/paper/2307.11017/) (2023)
- [SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-sampling from a Single Image](https://mlsanity.com/paper/2305.12646/) (2023)

## Abstract (arXiv)

Shape instantiation which predicts the 3D shape of a dynamic target from one or more 2D images is important for real-time intra-operative navigation. Previously, a general shape instantiation framework was proposed with manual image segmentation to generate a 2D Statistical Shape Model (SSM) and with Kernel Partial Least Square Regression (KPLSR) to learn the relationship between the 2D and 3D SSM for 3D shape prediction. In this paper, the two-stage shape instantiation is improved to be one-stage. PointOutNet with 19 convolutional layers and three fully-connected layers is used as the network structure and Chamfer distance is used as the loss function to predict the 3D target point cloud from a single 2D image. With the proposed one-stage shape instantiation algorithm, a spontaneous image-to-point cloud training and inference can be achieved. A dataset from 27 Right Ventricle (RV) subjects, indicating 609 experiments, were used to validate the proposed one-stage shape instantiation algorithm. An average point cloud-to-point cloud (PC-to-PC) error of 1.72mm has been achieved, which is comparable to the PLSR-based (1.42mm) and KPLSR-based (1.31mm) two-stage shape instantiation algorithm.