RealPoint3D: Point Cloud Generation from a Single Image with Complex Background

TL;DR

This paper introduces RealPoint3D, a novel method that generates accurate 3D point clouds from a single image with complex backgrounds by integrating prior 3D shape knowledge, overcoming limitations of previous approaches.

Contribution

The paper presents a new framework that combines retrieved 3D shape models with single images to improve 3D point cloud generation in complex real-world scenarios.

Findings

Achieves state-of-the-art accuracy in 3D point cloud generation.

Handles complex backgrounds and arbitrary viewpoints effectively.

Works well on real images with various backgrounds and angles.

Abstract

3D point cloud generation by the deep neural network from a single image has been attracting more and more researchers' attention. However, recently-proposed methods require the objects be captured with relatively clean backgrounds, fixed viewpoint, while this highly limits its application in the real environment. To overcome these drawbacks, we proposed to integrate the prior 3D shape knowledge into the network to guide the 3D generation. By taking additional 3D information, the proposed network can handle the 3D object generation from a single real image captured from any viewpoint and complex background. Specifically, giving a query image, we retrieve the nearest shape model from a pre-prepared 3D model database. Then, the image together with the retrieved shape model is fed into the proposed network to generate the fine-grained 3D point cloud. The effectiveness of our proposed framework has been verified on different kinds of datasets. Experimental results show that the proposed framework achieves state-of-the-art accuracy compared to other volumetric-based and point set generation methods. Furthermore, the proposed framework works well for real images in complex backgrounds with various view angles.