Abstract
Due to good scalability, systems for image-based dense surface reconstruction often employ stereo or multi-baseline stereo methods. These types of algorithms represent the scene by a set of depth or disparity maps which eventually have to be fused to extract a consistent, non-redundant surface representation. Generally the single depth observations across the maps possess variances in quality. Within the fusion process not only preservation of precision and detail but also density and robustness with respect to outliers are desirable. Being prune to outliers, in this article we propose a local median-based algorithm for the fusion of depth maps eventually representing the scene as a set of oriented points. Paying respect to scalability, points induced by each of the available depth maps are streamed to cubic tiles which then can be filtered in parallel. Arguing that the triangulation uncertainty is larger in the direction of image rays we define these rays as the main filter direction. Within an additional strategy we define the surface normals as the principle direction for median filtering/integration. The presented approach is straight-forward to implement since employing standard oc- and kd-tree structures enhanced by nearest neighbor queries optimized for cylindrical neighborhoods. We show that the presented method in combination with the MVS (Rothermel et al., 2012) produces surfaces comparable to the results of the Middlebury MVS benchmark and favorably compares to an state-of-the-art algorithm employing the Fountain dataset (Strecha et al., 2008). Moreover, we demonstrate its capability of depth map fusion for city scale reconstructions derived from large frame airborne imagery.
Highlights
A tremendous amount of research was conducted in the area of image-based surface reconstruction in the last three decades
In this work we focus on the problem of depth map fusion for a wide range of applications, including datasets in the domain of large scale airborne mapping
The problem of depth map fusion is aggravated by the large number and complex nature of effects influencing the variances of observations across depth maps
Summary
A tremendous amount of research was conducted in the area of image-based surface reconstruction in the last three decades. Typical representatives include level set methods (Pons et al, 2007) and mesh evolution algorithms (Hiep et al, 2009). The problem of depth map fusion is aggravated by the large number and complex nature of effects influencing the variances of observations across depth maps. These are e.g. geometric properties of camera network configuration like the number of images a point is seen in, the intersection angles of images rays, as well as errors from stereo matching like sub-pixel locking, fronto parallel effects or image blur as well as errors introduced by inaccuracies in bundle block adjustment. The double-blind peer-review was conducted on the basis of the full paper
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
