Conference Agenda

The rapid evolution of three-dimensional (3-D) geospatial science has redefined the standards of national mapping and cadastral agencies (NMCAs). Traditionally bodies of authoritative 2-D topographic products, these organisations now face the challenge of producing, maintaining, and disseminating national-scale 3-D geospatial datasets that support applications ranging from climate adaptation and urban planning to disaster response and digital twins. This paper presents a comparative study of five NMCAs, comprising IGN (France), BKG (Germany), Kadaster (The Netherlands), GSI (Japan) and USGS (United States of America). By examining agency structure, economic models, and 3-D data collection programmes, this paper identifies converging trends in AI integration, national surveys, along with divergences in funding and implementation. The analysis highlights insights and potential lessons for organisations at early stages of national 3-D dataset implementation.

Frame-wise semantic segmentation of indoor lidar scans is a fundamental step toward higher-level 3D scene understanding and mapping applications.

However, acquiring frame-wise ground truth for training deep learning models is costly and time-consuming. This challenge is largely addressed, for imagery, by Visual Foundation Models (VFMs) which segment image frames. The same VFMs may be used to train a lidar scan frame segmentation model via a 2D-to-3D distillation pipeline. The success of such distillation has been shown for autonomous driving scenes, but not yet for indoor scenes. Here, we study the feasibility of repeating this success for indoor scenes, in a frame-wise distillation manner by coupling each lidar scan with a VFM-processed camera image. The evaluation is done using indoor SLAM datasets, where pseudo-labels are used for downstream evaluation. Also, a small manually annotated lidar dataset is provided for validation, as there are no other lidar frame-wise indoor datasets with semantics. Results show that the distilled model

achieves up to 56% mIoU under pseudo-label evaluation and around 36% mIoU with real-label, demonstrating the feasibility of

cross-modal distillation for indoor lidar semantic segmentation without manual annotations.

Recent advances in image-based satellite 3D reconstruction have progressed along two complementary directions. On one hand, multi-date approaches using NeRF or Gaussian-splatting jointly model appearance and geometry across many acquisitions, achieving accurate reconstructions on opportunistic imagery with numerous observations. On the other hand, classical stereoscopic reconstruc- tion pipelines deliver robust and scalable results for simultaneous or quasi-simultaneous image pairs. However, when the two images are captured months apart, strong seasonal, illumination, and shadow changes violate standard stereoscopic assumptions, causing existing pipelines to fail. This work presents the first Diachronic Stereo Matching method for satellite imagery, enabling reliable 3D reconstruction from temporally distant pairs. Two advances make this possible: (1) fine-tuning a state-of-the-art deep stereo network that leverages monocular depth priors, and (2) exposing it to a dataset specifically curated to include a diverse set of diachronic image pairs. In particular, we start from a pretrained MonSter model, originally trained on a mix of synthetic and real datasets such as SceneFlow and KITTI, and fine-tune it on a set of stereo pairs derived from the DFC2019 remote sensing challenge. This dataset contains both synchronic and diachronic pairs under diverse seasonal and illumination conditions. Experiments on multi-date WorldView-3 imagery demonstrate that our approach consistently surpasses classical pipelines and unadapted deep stereo models on both synchronic and diachronic settings. Fine-tuning on temporally diverse images, together with monocular priors, proves essential for enabling 3D reconstruction from previously incompatible acquisition dates.

Unmanned Aerial Vehicles (UAV)-based photogrammetry provides an efficient solution for shallow water bathymetry, yet its accuracy is fundamentally constrained by light refraction at the air-water interface, which violates the central geometric assumptions of traditional photogrammetry.

Existing approaches, ranging from empirical corrections and iterative post-processing to black-box deep learning, often compromise geometric fidelity, physical interpretability, or generalization.

We address this challenge through Refraction-Aware Gaussian Splatting (RA-GS), which embeds a physically rigorous two-media refraction model directly into the Gaussian Splatting (GS) reconstruction pipeline.

Rather than relying on computationally expensive per-pixel ray tracing, we formulate an analytical parameter transformation that maps the true underwater position, scale, and opacity of each Gaussian to their apparent states observed through a planar refractive interface.

Through this fully differentiable transformation, true underwater 3D geometry and photorealistic appearance are jointly optimized by directly minimizing the photometric error within the standard GS framework.

This approach relies solely on RGB imagery, eliminating the need for external depth priors or deep learning networks.

Using a physically based, ray-traced synthetic riverbed dataset, we isolate and explicitly correct refractive distortions.

Our method achieves a geometric F1-score of 94\% (10 cm threshold at 10 m depth) and produces high-quality novel view synthesis with a PSNR of 25.9 dB and SSIM of 0.93.

Field experiments on real UAV data corroborate the practical utility for high-precision bathymetric mapping under calm-surface conditions.

By resolving the fundamental refractive difficulty, the proposed framework provides a physically grounded, computationally efficient, and practically useful solution for next-generation photogrammetric bathymetry.