A Scalable Collaborative Online System for City Reconstruction

3d Reconstruction With a Collaborative Approach Based on Smartphones and a Cloud-Based Server

“…Current 3D reconstruction solutions running on smartphones only offer feedback to single users during image acquisitions, and do not yet seamlessly include collaborative approaches with simultaneous feedback to the multiple. The most common solution for collaborative mapping, based either on Simultaneous Localization and Mapping (SLAM) or SfM approaches, is to produce separate maps that are finally fused together (Forster et al, 2013;Untzelmann et al, 2013;Morrison et al, 2016;Schmuck, 2017). The procedure presented in this paper is based on an incremental SfM approach (Schonberger and Frahm, 2016), which updates and augments the global sparse 3D point cloud when a new image is uploaded.…”

“…These methods implement very similar workflows, relying on Structure from Motion (SfM) and dense image matching (DIM) or Multi View Stereo (MVS) algorithms, run either on the phone or on a server. Being the 3D reconstruction procedure computationally intensive, a feasible solution is to split the process between the mobile device and the cloud-based server (Untzelmann et al, 2013;Locher et al, 2016c). In this case, the smartphone is used as imaging device to capture images of the scene of interest, whereas the SfM and DIM steps are performed on the server.…”

Progressive 3D Modeling All the Way

“…While incremental SfM can incorporate new images into an existing 3D model and provide an updated output soon after [16], current MVS algorithms cannot handle changes in the input calibration. Even the recently published Progressive Prioritized Multi-view Stereo (HPMVS) algorithm [10], which can deliver a progressive multiscale output, cannot handle changes in input data.…”

Integrating Smartphone Images and Airborne Lidar Data for Complete Urban Building Modelling

“…Let represents the normal of point in façade ground image point cloud { }. Similar to the method proposed by Untzelmann et al (2013), we obtain the initial up-right direction 0 by calculating the normal of a plane fitted from camera positions. After removing noise points where the angle between its normal and 0 is larger than a certain threshold, we apply a RANSAC-based approach to refine the up-right vector by iteratively selecting two points and from the façade points and estimating the cross product of and :…”

“…Geo-tags and text labels are commonly used for georegistration of models generated from ground images (Zamir and Shah, 2014;Zamir, 2014;Grzeszczuk et al, 2009), usually followed by an iterative refinement of aligning the models to building footprints. Furthermore, 3D models from Google Earth and building footprints from OpenStreetMap may also be used to improve geo-registration (Untzelmann et al, 2013;Wang et al, 2013).…”