# Fast-Image2Point: Towards Real-Time Point Cloud Reconstruction of a Single Image using 3D Supervision

arXiv: https://arxiv.org/abs/2209.10029 · HTML: https://mlsanity.com/paper/2209.10029/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction](https://mlsanity.com/paper/1604.00449/) (2016)
- [Atlas: End-to-End 3D Scene Reconstruction from Posed Images](https://mlsanity.com/paper/2003.10432/) (2020)
- [A guide to convolution arithmetic for deep learning](https://mlsanity.com/paper/1603.07285/) (2016)
- [Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era](https://mlsanity.com/paper/1906.06543/) (2019)
- [3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image](https://mlsanity.com/paper/1807.07796/) (2018)
- [Application of generative autoencoder in de novo molecular design](https://mlsanity.com/paper/1711.07839/) (2017)
- [Variational AutoEncoder For Regression: Application to Brain Aging Analysis](https://mlsanity.com/paper/1904.05948/) (2019)
- [DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image](https://mlsanity.com/paper/1708.04672/) (2017)
- [Modeling 3D Shapes by Reinforcement Learning](https://mlsanity.com/paper/2003.12397/) (2020)
- [An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion](https://mlsanity.com/paper/2008.10532/) (2020)
- [CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision](https://mlsanity.com/paper/1811.11731/) (2018)
- [Learning to Generate Dense Point Clouds with Textures on Multiple Categories](https://mlsanity.com/paper/1912.10545/) (2019)
- [Visual Enhanced 3D Point Cloud Reconstruction from A Single Image](https://mlsanity.com/paper/2108.07685/) (2021)

## Abstract (arXiv)

A key question in the problem of 3D reconstruction is how to train a machine or a robot to model 3D objects. Many tasks like navigation in real-time systems such as autonomous vehicles directly depend on this problem. These systems usually have limited computational power. Despite considerable progress in 3D reconstruction systems in recent years, applying them to real-time systems such as navigation systems in autonomous vehicles is still challenging due to the high complexity and computational demand of the existing methods. This study addresses current problems in reconstructing objects displayed in a single-view image in a faster (real-time) fashion. To this end, a simple yet powerful deep neural framework is developed. The proposed framework consists of two components: the feature extractor module and the 3D generator module. We use point cloud representation for the output of our reconstruction module. The ShapeNet dataset is utilized to compare the method with the existing results in terms of computation time and accuracy. Simulations demonstrate the superior performance of the proposed method. Index Terms-Real-time 3D reconstruction, single-view reconstruction, supervised learning, deep neural network