The german camera evaluation project - results from the geometry group

The so-called German camera evaluation project was initiated by the German society of Photogrammetry, Remote Sensing and Geoinformation (DGPF) in order to allow for comprehensive empirical test on photogrammetric digital airborne camera systems. During this test, the digital camera systems DMC, Ultracam-X, ADS40 (2nd generation), JAS-150, Quattro DigiCAM and AIC-x1 were flown in the test site Vaihingen/Enz in summer 2008. In addition, RMK analogue images and ALS50 LiDAR data were recorded for comparison, while reference measurements on the ground were made available as well. Parts of the test field were also covered from hyper-spectral sensor flights, namely the AISA+ and ROSIS system. After data collection all this material was prepared, documented and distributed to more than 30 institutions which participated in the evaluation and formed the project network of expertise. This evaluation phase included topics like the analysis of geometric accuracy and sensor calibration, the radiometric performance including on-site radiometric calibration and multi-spectral land classifications. Additionally, the performance of photogrammetric surface model generation and the potential of manual stereo plotting from digital images were investigated. Within this paper, the major findings from the geometric evaluations, namely sensor orientation and height model generation are presented

INDOOR MESH CLASSIFICATION FOR BIM

This work addresses the automatic reconstruction of objects useful for BIM, like walls, floors and ceilings, from meshed and textured mapped 3D point clouds of indoor scenes. For this reason, we focus on the semantic segmentation of 3D indoor meshes as the initial step for the automatic generation of BIM models. Our investigations are based on the benchmark dataset ScanNet, which aims at the interpretation of 3D indoor scenes. For this purpose it provides 3D meshed representations as collected from low cost range cameras. In our opinion such RGB-D data has a great potential for the automated reconstruction of BIM objects

Preface: Workshop “Semantics3D - Semantic Scene Analysis and 3D Reconstruction from Images and Image Sequences”

[no abstract available

Depth Supervised Neural Surface Reconstruction from Airborne Imagery

While originally developed for novel view synthesis, Neural Radiance Fields (NeRFs) have recently emerged as an alternative to multi-view stereo (MVS). Triggered by a manifold of research activities, promising results have been gained especially for textureless, transparent, and reflecting surfaces, while such scenarios remain challenging for traditional MVS-based approaches. However, most of these investigations focus on close-range scenarios, with studies for airborne scenarios still missing. For this task, NeRFs face potential difficulties at areas of low image redundancy and weak data evidence, as often found in street canyons, facades or building shadows. Furthermore, training such networks is computationally expensive. Thus, the aim of our work is twofold: First, we investigate the applicability of NeRFs for aerial image blocks representing different characteristics like nadir-only, oblique and high-resolution imagery. Second, during these investigations we demonstrate the benefit of integrating depth priors from tie-point measures, which are provided during presupposed Bundle Block Adjustment. Our work is based on the state-of-the-art framework VolSDF, which models 3D scenes by signed distance functions (SDFs), since this is more applicable for surface reconstruction compared to the standard volumetric representation in vanilla NeRFs. For evaluation, the NeRF-based reconstructions are compared to results of a publicly available benchmark dataset for airborne images

Design and Development of Personal GeoServices for Universities

Personal GeoServices are emerging as an interaction paradigm linking users to information rich environments like a university campus or to Big Data sources like the Internet of Things by delivering spatially intelligent web-services. OpenStreetMap (OSM) constitutes a valuable source of spatial base-data that can be extracted, integrated, and utilised with such heterogeneous data sources for free. In this paper, we present a Personal GeoServices application built on OSM spatial data and university-specific business data for staff, faculty, and students. While generic products such as Google Maps and Google Earth enable basic forms of spatial exploration, the domain of a university campus presents specific business information needs, such as “What classes are scheduled in that room over there?” and “How can I get to Prof. Murray’s office from here?” Within the framework of the StratAG project (www.StratAG.ie), an eCampus Demonstrator was developed for the National University of Ireland Maynooth (NUIM) to assist university users in exploring and analysing their surroundings within a detailed data environment. This work describes this system in detail, discussing the usage of OSM vector data, and providing insights for developers of spatial information systems for personalised visual exploration of an area

A mobile multi-sensor platform for building reconstruction integrating terrestrial and autonomous UAV-based close range data acquisition

Photogrammetric data capture of complex 3D objects using UAV imagery has become commonplace. Software tools based on algorithms like Structure-from-Motion and multi-view stereo image matching enable the fully automatic generation of densely meshed 3D point clouds. In contrast, the planning of a suitable image network usually requires considerable effort of a human expert, since this step directly influences the precision and completeness of the resulting point cloud. Planning of suitable camera stations can be rather complex, in particular for objects like buildings, bridges and monuments, which frequently feature strong depth variations to be acquired by high resolution images at a short distance. Within the paper, we present an automatic flight mission planning tool, which generates flight lines while aiming at camera configurations, which maintain a roughly constant object distance, provide sufficient image overlap and avoid unnecessary stations. Planning is based on a coarse Digital Surface Model and an approximate building outline. As a proof of concept, we use the tool within our research project MoVEQuaD, which aims at the reconstruction of building geometry at sub-centimetre accuracy

Preface: Workshop “Semantics3D - Semantic Scene Analysis and 3D Reconstruction from Images and Image Sequences”

[no abstract available

ULTRA-HIGH PRECISION UAV-BASED LIDAR AND DENSE IMAGE MATCHING

This paper presents a study on the potential of ultra-high accurate UAV-based 3D data capture. It is motivated by a project aiming at the deformation monitoring of a ship lock and its surrounding. This study is part of a research and development project initiated by the German Federal Institute of Hydrology (BfG) in Koblenz in partnership with the Office of Development of Neckar River Heidelberg (ANH). For this first official presentation of the project, data from the first flight campaign will be analysed and presented. Despite the fact that monitoring aspects cannot be discussed before data from additional flight campaigns will be available later this year, our results from the first campaign highlight the potential of high-end UAV-based image and LiDAR sensors and their data fusion. So far, only techniques from engineering geodesy could fulfil the aspired accuracy demands in the range of millimetres. To the knowledge of the authors, this paper for the first time addresses such ultra-high accuracy applications by combing high precision UAV-based LiDAR and dense image matching. As the paper is written at an early stage of processing only preliminary results can be given here

GEOMETRIC PROCESSING OF VERY HIGH-RESOLUTION SATELLITE IMAGERY: QUALITY ASSESSMENT FOR 3D MAPPING NEEDS

In recent decades, the geospatial domain has benefitted from technological advances in sensors, methodologies, and processing tools to expand capabilities in mapping applications. Airborne techniques (LiDAR and aerial photogrammetry) generally provide most of the data used for this purpose. However, despite the relevant accuracy of these technologies and the high spatial resolution of airborne data, updates are not sufficiently regular due to significant flight costs and logistics. New possibilities to fill this information gap have emerged with the advent of Very High Resolution (VHR) optical satellite images in the early 2000s. In addition to the high temporal resolution of the cost-effective datasets and their sub-meter geometric resolutions, the synoptic coverage is an unprecedented opportunity for mapping remote areas, multi-temporal analyses, updating datasets and disaster management. For all these reasons, VHR satellite imagery is clearly a relevant study for National Mapping and Cadastral Agencies (NMCAs). This work, supported by EuroSDR, summarises a series of experimental analyses carried out over diverse landscapes to explore the potential of VHR imagery for large-scale mapping

SLAM for Indoor Mapping of Wide Area Construction Environments

Simultaneous localization and mapping (SLAM), i.e., the reconstruction of the environment represented by a (3D) map and the concurrent pose estimation, has made astonishing progress. Meanwhile, large scale applications aiming at the data collection in complex environments like factory halls or construction sites are becoming feasible. However, in contrast to small scale scenarios with building interiors separated to single rooms, shop floors or construction areas require measures at larger distances in potentially texture less areas under difficult illumination. Pose estimation is further aggravated since no GNSS measures are available as it is usual for such indoor applications. In our work, we realize data collection in a large factory hall by a robot system equipped with four stereo cameras as well as a 3D laser scanner. We apply our state-of-the-art LiDAR and visual SLAM approaches and discuss the respective pros and cons of the different sensor types for trajectory estimation and dense map generation in such an environment. Additionally, dense and accurate depth maps are generated by 3D Gaussian splatting, which we plan to use in the context of our project aiming on the automatic construction and site monitoring