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

TL;DR

This paper introduces a fast deep learning framework for real-time 3D point cloud reconstruction from a single image, suitable for applications like autonomous navigation with limited computational resources.

Contribution

It presents a novel, efficient neural network architecture that significantly improves speed and accuracy in single-view 3D reconstruction using 3D supervision.

Findings

Outperforms existing methods in speed and accuracy on ShapeNet dataset

Demonstrates real-time capability suitable for autonomous systems

Uses point cloud output for effective 3D modeling

Abstract

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