Overview

We present a new optimization parsing framework for the geometric analysis of a single image coming from a man-made environment. This framework models the scene as a composition of geometric primitives spanning different layers from low level (edges) through mid-level (lines segments, lines and vanishing points) to high level (the zenith and the horizon). The inference in such a model thus jointly and simultaneously estimates a) the grouping of edges into the line segments, b) the grouping of line segments into the straight lines, c) the grouping of lines into parallel families, and d) the positioning of the horizon and the zenith in the image. Such a unified treatment means that the uncertainty information propagates between the layers of the model. This is in contrast to most previous approaches to the same problem, which either ignore the middle levels (line segments or lines) all together, or use the bottom-up step-by-step pipeline.

For the evaluation, we consider a publicly available York Urban dataset of ``Manhattan'' scenes, and also introduce a new, harder dataset of 103 urban outdoor images containing many non-Manhattan scenes. The comparative evaluation for the horizon estimation task demonstrate higher accuracy and robustness attained by our method when compared to the current state-of-the-art approaches.

Previous works use step-by-step approach:

We propose single unified model for geometric parsing:

Sourñe Code

By installing, copying, or otherwise using this Software, you agree to be bound by the terms of the Microsoft Research Shared Source License Agreement (non-commercial use only). If you do not agree, do not install copy or use the Software. The Software is protected by copyright and other intellectual property laws and is licensed, not sold.

The software comes “as is”, with no warranties. This means no express, implied or statutory warranty, including without limitation, warranties of merchantability or fitness for a particular purpose, any warranty against interference with your enjoyment of the software or any warranty of title or non-infringement. There is no warranty that this software will fulfill any of your particular purposes or needs. Also, you must pass this disclaimer on whenever you distribute the software or derivative works.

Neither Microsoft nor any contributor to the software will be liable for any damages related to the software or this msr-ssla, including direct, indirect, special, consequential or incidental damages, to the maximum extent the law permits, no matter what legal theory it is based on. Also, you must pass this limitation of liability on whenever you distribute the software or derivative works.

When using this software, please acknowledge the effort that went into development by referencing the corresponding papers listed in README.txt in the archive.

If you have questions concerning the source code, please contact obarinova [at] graphics [dot] cs [dot] msu [dot] ru and tretiak.elena [at] gmail [dot] com.

Download:

1. ECCV paper version
2. IJCV paper extended version (version without libraries - smaller)

Download York Urban DB

Eurasian Cities Dataset

Download Eurasian Cities Database with annotations introduced in the paper.

Ó÷àñòíèêè

• Îëüãà Áàðèíîâà, Lomonosov Moscow State University
• Victor Lempitsky, Yandex Moscow
• Elena Tretyak, MPI Tuebingen
• Pushmeet Kohli, Microsoft Research Cambridge

Ïóáëèêàöèè

• Tretiak E, Barinova O, Kohli P, Lempitsky V. Geometric image parsing in man-made environments. IJCV, 2011 (download)
• Barinova O, Lempitsky V, Tretiak E, Kohli P. Geometric Image Parsing in Man?Made Environments. European Conference on Computer Vision, 2010 (download)

Áëàãîäàðíîñòè

This project is supported by Microsoft Research programs in Russia.