Markus Rumpler

Online Feedback for Structure-from-Motion Image Acquisition

The quality and completeness of 3D models obtained by Structure-fromMotion (SfM) heavily depend on the image acquisition process. If the user gets feedback about the reconstruction quality already during the acquisition, he can optimize this process. The goal of this paper is to support a user during image acquisition by giving online feedback of the current reconstruction quality. We propose an online SfM method that integrates wide-baseline still-images in an online fashion into a consistent reconstruction and we derive a surface model given the SfM point cloud. To guide the user to scene parts that are captured not very well, we colour the mesh according to redundancy and resolution information. In the experiments, we show that our approach makes the final SfM result predictable already during image acquisition. The method is suited for large-scale reconstructions as obtained by flying micro aerial vehicles as well as on small indoor environments. We propose a method that supports a user in the acquisition process in two ways: (a) sparse online SfM with accuracy close to offline methods and (b) surface extraction and quality visualization. The workflow of our method is shown in Figure 1.

Evaluations on multi-scale camera networks for precise and geo-accurate reconstructions from aerial and terrestrial images with user guidance

Use of planar fiducial markers for automatic accurate camera calibration.Online user feedback and quality visualization for image acquisition.Integration of ground control points and GPS measurements in the bundle adjustment.Accurate and easy-to-use 3D reconstruction pipeline with automatic geo-registration.Unified document with extensive evaluations and insights to large-scale 3D modeling. During the last decades photogrammetric computer vision systems have been well established in scientific and commercial applications. Recent developments in image-based 3D reconstruction systems have resulted in an easy way of creating realistic, visually appealing and accurate 3D models. We present a fully automated processing pipeline for metric and geo-accurate 3D reconstructions of complex geometries supported by an online feedback method for user guidance during image acquisition. Our approach is suited for seamlessly matching and integrating images with different scales, from different view points (aerial and terrestrial), and with different cameras into one single reconstruction. We evaluate our approach based on different datasets for applications in mining, archaeology and urban environments and thus demonstrate the flexibility and high accuracy of our approach. Our evaluation includes accuracy related analyses investigating camera self-calibration, georegistration and camera network configuration.

Using Self-Contradiction to Learn Confidence Measures in Stereo Vision

Learned confidence measures gain increasing importance for outlier removal and quality improvement in stereo vision. However, acquiring the necessary training data is typically a tedious and time consuming task that involves manual interaction, active sensing devices and/or synthetic scenes. To overcome this problem, we propose a new, flexible, and scalable way for generating training data that only requires a set of stereo images as input. The key idea of our approach is to use different view points for reasoning about contradictions and consistencies between multiple depth maps generated with the same stereo algorithm. This enables us to generate a huge amount of training data in a fully automated manner. Among other experiments, we demonstrate the potential of our approach by boosting the performance of three learned confidence measures on the KITTI2012 dataset by simply training them on a vast amount of automatically generated training data rather than a limited amount of laser ground truth data.

Probabilistic Range Image Integration for DSM and True-Orthophoto Generation

Typical photogrammetric processing pipelines for digital surface model (DSM) generation perform aerial triangulation, dense image matching and a fusion step to integrate multiple depth estimates into a consistent 2.5D surface model. The integration is strongly influenced by the quality of the individual depth estimates, which need to be handled robustly. We propose a probabilistically motivated 3D filtering scheme for range image integration. Our approach avoids a discrete voxel sampling, is memory efficient and can easily be parallelized. Neighborhood information given by a Delaunay triangulation can be exploited for photometric refinement of the fused DSMs before rendering true-orthophotos from the obtained models. We compare our range image fusion approach quantitatively on ground truth data by a comparison with standard median fusion. We show that our approach can handle a large amount of outliers very robustly and is able to produce improved DSMs and true-orthophotos in a qualitative comparison with current state-of-the-art commercial aerial image processing software.

UAV-Based Autonomous Image Acquisition with Multi-view Stereo Quality Assurance by Confidence Prediction

In this paper we present an autonomous system for acquiring close-range high-resolution images that maximize the quality of a later-on 3D reconstruction with respect to coverage, ground resolution and 3D uncertainty. In contrast to previous work, our system uses the already acquired images to predict the confidence in the output of a dense multi-view stereo approach without executing it. This confidence encodes the likelihood of a successful reconstruction with respect to the observed scene and potential camera constellations. Our prediction module runs in real-time and can be trained without any externally recorded ground truth. We use the confidence prediction for on-site quality assurance and for planning further views that are tailored for a specific multi-view stereo approach with respect to the given scene. We demonstrate the capabilities of our approach with an autonomous Unmanned Aerial Vehicle (UAV) in a challenging outdoor scenario.

Geo-referenced 3D reconstruction: Fusing public geographic data and aerial imagery

We present an image-based 3D reconstruction pipeline for acquiring geo-referenced semi-dense 3D models. Multiple overlapping images captured from a micro aerial vehicle platform provide a highly redundant source for multi-view reconstructions. Publicly available geo-spatial information sources are used to obtain an approximation to a digital surface model (DSM). Models obtained by the semi-dense reconstruction are automatically aligned to the DSM to allow the integration of highly detailed models into the original DSM and to provide geographic context.