Simplifying Urban Data Fusion with BigSUR

TL;DR

This paper improves the BigSUR urban reconstruction system by removing the need for street-level images, speeding up processing, and maintaining high accuracy in semantic labelling of urban environments.

Contribution

The authors simplify and validate BigSUR by eliminating street-level images and introducing a greedy post-process, reducing computational time while preserving accuracy.

Findings

Achieves accurate semantic labelling with less data

Reduces processing time from 15 hours to shorter durations

Maintains high reconstruction accuracy despite simplifications

Abstract

Our ability to understand data has always lagged behind our ability to collect it. This is particularly true in urban environments, where mass data capture is particularly valuable, but the objects captured are more varied, denser, and complex. To understand the structure and content of the environment, we must process the unstructured data to a structured form. BigSUR is an urban reconstruction algorithm which fuses GIS data, photogrammetric meshes, and street level photography, to create clean representative, semantically labelled, geometry. However, we have identified three problems with the system i) the street level photography is often difficult to acquire; ii) novel fa\c{c}ade styles often frustrate the detection of windows and doors; iii) the computational requirements of the system are large, processing a large city block can take up to 15 hours. In this paper we describe the process of simplifying and validating the BigSUR semantic reconstruction system. In particular, the requirement for street level images is removed, and greedy post-process profile assignment is introduced to accelerate the system. We accomplish this by modifying the binary integer programming (BIP) optimization, and re-evaluating the effects of various parameters. The new variant of the system is evaluated over a variety of urban areas. We objectively measure mean squared error (MSE) terms over the unstructured geometry, showing that BigSUR is able to accurately recover omissions from the input meshes. Further, we evaluate the ability of the system to label the walls and roofs of input meshes, concluding that our new BigSUR variant achieves highly accurate semantic labelling with shorter computational time and less input data.