Conference Agenda

This work presents a multi-camera mobile mapping solution designed to deliver accurate and efficient 3D reconstructions across a wide variety of challenging environments, ranging from confined indoor spaces to complex urban outdoor settings. Traditional photogrammetric and terrestrial laser scanning approaches, while capable of high accuracy, often suffer from limitations related to acquisition speed, logistical complexity, and significant post-processing effort—especially in large, occluded, or hard-to-access sites. Mobile Mapping Systems (MMS) based on Visual SLAM (V-SLAM) offer a compelling alternative, thanks to their ability to acquire high-frequency imagery in continuous motion and estimate sensor trajectories in real-time. However, MMS outputs frequently face issues such as reduced geometric accuracy, scale drift in monocular sequences, and the need for extensive optimisation to reach survey-grade results.

To address these limitations, the study extends an existing multi-camera V-SLAM pipeline by tightly integrating monocular estimates with multi-stereo trajectories within the ATOM-ANT3D fisheye multi-camera system. A novel monocular scale-recovery strategy is introduced, based on path-length ratios derived from concurrently recorded stereo tracks. This metrized monocular trajectory is then fused with stereo estimates through a robust pose graph optimisation, followed by a multi-view, feature-based refinement leveraging pre-calibrated camera geometry.

The proposed method is evaluated across four real-world scenarios—spiral tower staircases, dark underground caves, narrow urban corridors, and constrained industrial pipelines. Accuracy is assessed against reference 3D point clouds, while efficiency is compared to a standard multi-view stereo photogrammetric pipeline. Results demonstrate that the integrated approach significantly improves reconstruction consistency, robustness, and end-to-end throughput.

Traditional image matching methods rely heavily on gradient or intensity information. However, the severe nonlinear radiometric distortion (NRD) between infrared and visible images hinders the extraction of repeatable feature points, leading to poor matching performance. To address this, we propose Shape2Match, a novel framework that replaces point features with more consistent, modality-invariant shape features. Specifically, the method utilizes EfficientSAM to extract shape contours and employs elliptic fourier descriptors (EFD) to parameterize and normalize them, creating shape descriptor that is invariant to translation, rotation, and scale. Shape2Match adopts a coarse-to-fine hierarchical strategy: it first performs robust global shape matching using a weighted EFD distance, followed by precise keypoint matching—using Shape Context—within the coarsely aligned shape pairs. We validated Shape2Match on 153 image pairs from 6 datasets, comparing it against methods like SIFT, RIFT, and MS-HLMO. Experimental results demonstrate that Shape2Match achieves a 100\% success rate (SR) across all datasets and significantly outperforms other methods in the number of correct matches (NCM), proving its effectiveness and robustness against NRD, rotation, and scale variations.

Historical film-based images, acquired by aerial sensors since the 1930s and by satellite platforms since the 1960s, provide a unique opportunity to document changes in the Earth surface over the 20th century. Yet, they present significant and specific challenges, including complex distortion in the scanned image pixel grid and poorly known camera exterior and interior orientation. In recent years, semi- or fully-automated approaches, based on photogrammetric and computer vision methods, have emerged, but the performance and limitations of these methods have yet to be directly compared.

The objectives of the Historical Images for Surface Topography Reconstruction Intercomparison eXperiment (Historix) project are to compare existing methods for processing stereoscopic historical images and harmonize processing tools. Here we present the study site and dataset selected for this comparison, the design of the intercomparison and evaluation metrics, as well as preliminary results. Full evaluation will be presented at the conference.

The location accuracy of an image acquired with a space borne camera relies on the knowledge of the orbit of the spacecraft and the orientation of the camera. The a posteriori estimation of a satellite orbit has been a well mastered technique for a long time. Sub-meter accuracy is achievable with a reasonable effort. The geolocation, with a similar accuracy, of the line of sight of an optical instrument flying at 500km or above is a much more challenging task..

On the other hand, the geolocation of a synthetic aperture radar (SAR) image depends only on the orbit of the spacecraft. It is, therefore, easy to acquire space borne SAR images with a sub-metric native geolocation. The Airbus SAR constellation (TerraSAR-X, TanDEM-X and PAZ) provides, on a commercial basis, images with a (better than) 0.2m geolocation accuracy.

The ability to find, through image matching, homologous points in SAR and optical images would transfer the native accuracy of SAR to optical observations, using classical photogrammetric bundle adjustment.

This paper describes an operational way to perform this SAR/Optic images matching and a validation of the absolute location accuracy achieved.

High-precision mapping of planetary surfaces, such as Mars, relies on matched control points derived from existing georeferenced data, as ground control points (GCPs) cannot be obtained through field measurement. However, the handcrafted image matchers like SIFT limit the robustness of this approach, particularly on texture-scarce and self-similar Martian terrain. While deep learning-based matchers offer a new paradigm, their performance gain for bundle adjustment remains inadequately quantified. This paper systematically evaluates four matchers (hand-crafted SIFT and deep learning-based DOG+HardNet+LightGlue, DISK+LightGlue, and LoFTR), assessing their impact on georeferencing tasks using Tianwen-1 high-resolution imagery. Deep learning methods, such as LoFTR, generate more correspondence points with a more uniform spatial distribution, halving the outlier rate and improving bundle adjustment accuracy by 10%. Our study provides a benchmark for planetary mapping and shows that powerful, learning-based image matchers are pivotal for next-generation automated mapping systems.

This work investigates the solutions developed at Natural Resources Canada to produce analysis-ready mapping products from Canada's national air photo library including two main workflows: 1) The photogrammetric processing of historical photos with an emphasis on the more challenging automated georeferencing component; 2) Enhancing interpretability through generative artificial intelligence models for super-resolution and deep colorization.

The accuracy of the aerial triangulation (AT) performed in the map projection for a GNSS-INS-supported image block consisting of 4342 vertical images, GSD 20 cm, with 22 main strips and 5 cross strips is investigated. Using 169 check points the obtained results are compared with the accuracy achieved by running the AT in an undistorted tangential system. It turns out, that in both systems the same accuracies can be achieved, with RMSE in (X, Y, Z) of (7, 10, 11) cm, if Earth curvature and scale distortion are correctly modelled in the map projection. If the scale distortion is not considered, then the RMSE in Z increases by 100% to 300% (depending on the height distribution of the GCPs). In AT software packages, that do not consider the scale distortion, a partial compensation is possible by either adapting the height of the projection centres or the principal distance leading to RMSE of around (10, 11, 15) cm.