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

arXiv 2209.10029 ↗First posted 20 Sep 2022cs.CVcited by 0

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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

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References · 18

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  4. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  5. 2018Open3D: A Modern Library for 3D Data Processingcited by 717
  6. 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 587
  7. 2020Atlas: End-to-End 3D Scene Reconstruction from Posed Imagescited by 206
  8. 2016A guide to convolution arithmetic for deep learningcited by 182
  9. 2019Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Eracited by 87
  10. 20183D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Imagecited by 40
  11. 2017Application of generative autoencoder in de novo molecular designcited by 32
  12. 2019Variational AutoEncoder For Regression: Application to Brain Aging Analysiscited by 30
6 more
  1. 2017DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Imagecited by 25
  2. 2020Modeling 3D Shapes by Reinforcement Learningcited by 20
  3. 2020An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusioncited by 19
  4. 2018CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervisioncited by 12
  5. 2019Learning to Generate Dense Point Clouds with Textures on Multiple Categoriescited by 6
  6. 2021Visual Enhanced 3D Point Cloud Reconstruction from A Single Imagecited by 2

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