Learning to Parse Wireframes in Images of Man-Made Environments

TL;DR

This paper introduces a learning-based method for automatically extracting wireframes from images of man-made environments, using a large labeled dataset and CNNs to improve line and junction detection for various visual tasks.

Contribution

The authors created a large new dataset of over 5,000 images with human-labeled wireframes and developed two CNNs that outperform existing methods in detecting lines and junctions.

Findings

Significantly improved detection accuracy over state-of-the-art methods.

Large-scale dataset enables robust learning for wireframe extraction.

Effective wireframe parsing benefits tasks like 3D reconstruction and navigation.

Abstract

In this paper, we propose a learning-based approach to the task of automatically extracting a "wireframe" representation for images of cluttered man-made environments. The wireframe (see Fig. 1) contains all salient straight lines and their junctions of the scene that encode efficiently and accurately large-scale geometry and object shapes. To this end, we have built a very large new dataset of over 5,000 images with wireframes thoroughly labelled by humans. We have proposed two convolutional neural networks that are suitable for extracting junctions and lines with large spatial support, respectively. The networks trained on our dataset have achieved significantly better performance than state-of-the-art methods for junction detection and line segment detection, respectively. We have conducted extensive experiments to evaluate quantitatively and qualitatively the wireframes obtained by our method, and have convincingly shown that effectively and efficiently parsing wireframes for images of man-made environments is a feasible goal within reach. Such wireframes could benefit many important visual tasks such as feature correspondence, 3D reconstruction, vision-based mapping, localization, and navigation. The data and source code are available at https://github.com/huangkuns/wireframe.