Mathias Rothermel

Dense Multi-Stereo Matching for High Quality Digital Elevation Models

gorithms. Airborne imagery of good dynamic range and signal-to-noise ratio as it is generated by digital aerial cameras is highly beneicial for automatic image matching. This is especially true for surfaces with relatively little surface texture. Consequently, the quality and precision of image-based point transfer as the basis for 3D surface reconstruction has been improved considerably. Recent tests have already demonstrated the feasibility of image

Extracting 3D urban models from oblique aerial images

Oblique airborne cameras are increasingly used for area covering image collection of building facades in urban environments. Until recently, these images were mainly used to improve the visual appearance of relatively simple 3D building models by providing suitable facade texture. Meanwhile, oblique airborne images are also used to generate dense 3D point clouds using state-of-the-art pixel-based multi-stereo image matching. Subsequently, these point clouds can then enhance the amount of geometric and semantic detail of 3D urban models especially for the depicted building facades. However, occlusions and large view-point changes are especially demanding during dense image matching can be very challenging for oblique imagery in complex urban environment. As described in the paper, our matching pipeline tackles these problems by a coarse-to-fine modification of the SGM method. Furthermore the raw 3D point clouds are efficiently analyzed and filtered in subsequent steps, thus 3D data capture from oblique airborne imagery while aiming at the extraction of 3D urban models becomes feasible.

Fast and Robust Generation of Semantic Urban Terrain Models from UAV Video Streams

We present an algorithm for extracting Level of Detail 2 (LOD2) building models from video streams captured by Unmaned Aerial Vehicles (UAVs). Typically, such imagery is of limited radiometric quality but the surface is captured with large redundancy. The first contribution of this paper is a novel algorithm exploiting this redundancy for precise depth computation. This is realized by fusing consistent depth estimations across single stereo models and generating a 2.5D elevation map from the resulting point clouds. Disparity maps are derived by a coarse-to-fine Semi-Global-Matching (SGM) method performing well on noisy imagery. The second contribution concerns a challenging step of the context-based urban terrain modeling: Dominant planes extraction for building reconstruction. Because of noisy data and complicated roof structures, both dominant plane parameters and initial values for support sets of planes are obtained by the J-Linkage algorithm. An improved point-to-plane labeling is presented to encourage the assignment of proximate points to the same plane. This is accomplished by non-local, Markov Random Field (MRF) - based optimization and segmentation of color information. The potential and the limitations of the proposed methods are shown using an UAV video sequence of limited radiometric quality.

Complementing TLS Point Clouds by Dense Image Matching

The complementation of terrestrial laser scanning (TLS) by photogrammetric acquisition techniques enables the exploitation of two different measurement principles. TLS with distance measurements can be used for acquiring large-scale point clouds at medium range distances, while image-based surface reconstruction methods enable flexible acquisition with high precision at short distances. Within this paper, an automatic procedure for the combination of photogrammetry and laser scanning is presented. Synthetic images derived from the laser scanning point clouds are used together with camera images in a common Structure-from- Motion (SfM) process. This enables an automatic registration of multiple laser scans. Furthermore, surface information can also be derived from the imagery using dense image matching methods. While the laser scanning data provides the missing scale information, the imagery can complement the dataset to fill gaps, occlusions or to resolve small details. Consequently, large datasets can be...

4D reconstruction of the past: the image retrieval and 3D model construction pipeline

One of the main characteristics of the Internet era we are living in, is the free and online availability of a huge amount of data. This data is of varied reliability and accuracy and exists in various forms and formats. Often, it is cross-referenced and linked to other data, forming a nexus of text, images, animation and audio enabled by hypertext and, recently, by the Web3.0 standard. Our main goal is to enable historians, architects, archaeolo- gists, urban planners and affiliated professionals to reconstruct views of historical monuments from thousands of images floating around the web. This paper aims to provide an update of our progress in designing and imple- menting a pipeline for searching, filtering and retrieving photographs from Open Access Image Repositories and social media sites and using these images to build accurate 3D models of archaeological monuments as well as enriching multimedia of cultural / archaeological interest with metadata and harvesting the end products to EU- ROPEANA. We provide details of how our implemented software searches and retrieves images of archaeological sites from Flickr and Picasa repositories as well as strategies on how to filter the results, on two levels; a) based on their built-in metadata including geo-location information and b) based on image processing and clustering techniques. We also describe our implementation of a Structure from Motion pipeline designed for producing 3D models using the large collection of 2D input images (>1000) retrieved from Internet Repositories.