Conference Agenda

Among the image-based methods, traditional photogrammetry is a consolidated 3D reconstruction technique able to provide highly accurate metric products, widely exploited in many domains for documentation and mapping purposes. The reconstruction capability of this technique is, however, conditioned by the characteristics of the captured scene, with high performance in well-textured areas and limits when non-collaborative surfaces, such as reflective or transparent objects, are present. In such cases, the photogrammetric reconstruction is often affected by noise, incomplete geometry and artifacts, reducing its final reconstruction quality. In recent years, different AI-based reconstruction methods have emerged as alternative (or complementary) 3D reconstruction and rendering solutions. In particular, 3D Gaussian Splatting (GS) has demonstrated impressive capabilities in rendering photorealistic scenes in challenging situations with high visual fidelity. However, its application in large-scale scenarios or when highly accurate 3D metric products are required is still limited, due to the hight computational resources needed and the intrinsic optimization of GS methods for photometric rendering quality. To address these bottlenecks, this work proposes a hybrid reconstruction pipeline, leveraging the strengths and benefits of each technique. The method exploits the accurate geometry of photogrammetry in well-textured regions and the higher GS capabilities to improve completeness and visual aspect in areas featuring non-collaborative surfaces. A fusion strategy is proposed to combine the two products into a single 3D model, presenting results on two aerial and one terrestrial dataset.

Neural Radiance Fields (NeRFs) (Mildenhall et al., 2020) have revolutionized novel view synthesis, but standard formulations assume straight rays in a single, homogeneous medium. In underwater scenarios, refraction at the air–water interface leads to bent light paths and, if ignored, to distorted 3D structure and missing underwater points. Refraction-aware NeRF variants such as NeRFrac (Xue et al.,2022) demonstrate the benefit of modeling refraction, but are limited to a single underwater medium and standalone implementations. Recent work has applied NeRFrac to through-water reconstruction (Brezovsky et al., 2025), introduced a simulation framework for two-media scenes (Schulte et al., 2025). Building on these ideas, we introduce the general concept of a twomedia NeRF and demonstrate its integration into the Nerfstudio framework (Tancik et al., 2023) with the goal of extracting metrically meaningful underwater point clouds rather than only improving image-based metrics.

Many geospatial workflows critically depend on high-fidelity 3D point clouds for applications such as change detection, orthophoto generation, and modeling. However, NeRF-generated point clouds often suffer from sampling inefficiencies inherent in the predominant random pixel sampling approach. We identify spatial redundancy as one such inefficiency: random sampling has the inevitable consequence of sampling large, low-texture patches more frequently than detailed, high-frequency textured regions. As a result, low-texture areas turn to be oversampled and other pixels remain unsampled -- regardless of their importance to the reconstruction task.

To overcome this, we propose CENS (Coverage-Efficient Non-Redundant Sampling), a deterministic pixel sampling strategy that maximizes spatial coverage, eliminates intra-image sample repetition, and ensures reproducibility via structured initialization.

Evaluated on the Jamtal valley dataset, CENS achieves comparable geometric accuracy (C2M: mean = -0.0027 vs. -0.0011 m; standard deviation = 0.027 vs. 0.028 m) using 50% fewer training steps (11,232 vs. 22,464), while yielding 28.2% more points, higher orthophoto fidelity, and improved point cloud completeness. Beyond CENS, we also explored NeRFs for ALS point cloud simulation, achieving realistic occlusion patterns and accuracy within UAV photogrammetry standards (Vertical RMSE} = 24 mm; Horizontal RMSE = 17 mm).

Crucially, CENS positions NeRFs as a scalable, practical solution for geospatial point cloud and orthophoto generation, advancing them toward real-world mapping workflows, and integrates seamlessly into NeRFStudio.

This research presents an innovative multi-modal framework for the explicit 3D reconstruction of building thermal environments to diagnose facade defects. We propose a framework which is centered on a dual-branch Neural Radiance Field (NeRF) architecture, which effectively fuses fine-grained geometric information from RGB data with precise quantitative thermal data from TIR data. For practical diagnostics, the framework integrates the Signed Distance Function (SDF) to implicitly learn a high-fidelity surface representation. Subsequently, a final, explicit triangular mesh is extracted from this implicit field using the Marching Cubes algorithm. The resulting model achieves geometric accuracy and thermal fidelity, enabling the clear visualization, localization, and analysis of thermal anomalies such as thermal bridges, cavities, and moisture ingress in their correct spatial context.

3D vision foundation models, which directly regress 3D geometry from 2D images in an end-to-end manner, have recently attracted growing attention in the computer vision community. However, their potential for oblique 3D reconstruction has not been systematically evaluated. To this end, we establish an automated evaluation pipeline to benchmark these models on oblique imagery. Our experiments reveal that: benefiting from the powerful zero-shot generalization, 3D vision foundation models can robustly estimate camera parameters and generate dense point clouds under sparse-view and low-overlap conditions, with some rivaling traditional photogrammetry configured with redundant observations. Counterintuitively, two-view reasoning foundation models employing explicit PnP-RANSAC for global alignment consistently outperform multi-view reasoning foundation models inferring multi-view relationships via implicit attention mechanism when processing more than 2 views. Notably, incorporating known camera parameters as conditioning inputs, which act as weak supervision rather than rigid geometric constraints, yields only marginal accuracy improvements. Based on ViT architecture, these foundation models face scalability bottlenecks to large-scale and high-resolution oblique imagery, and their prevalent ideal pinhole camera assumption still makes explicit distortion correction an unavoidable preprocessing step.

Three-dimensional (3D) reconstruction from satellite imagery is a critical research topic in the fields of remote sensing and geoinformation science. Although 3D Vision Foundation Models (3D VFMs) have demonstrated remarkable performance in reconstructing natural scenes, their capability to handle high-resolution satellite imagery has not been systematically evaluated. This study presents a comprehensive assessment of seven representative 3D VFMs for satellite-based 3D reconstruction and integrates four point-cloud alignment strategies. Rigorous comparisons were conducted against high-precision LiDAR-derived Digital Surface Models (DSMs) using two publicly available multi-view satellite datasets--WHU-TLC and MVS3D. Experimental results show that, on the high-resolution MVS3D dataset, the Depth Anything v2 (DAV2) model combined with the Affine alignment strategy achieved the best overall performance, producing DSMs with a Mean Absolute Error (MAE) of 1.75 m and a Root Mean Square Error (RMSE) of 3.24 m, corresponding to accuracy improvements of 8.4 % and 13.6 %, respectively--significantly outperforming all other model-strategy combinations. In contrast, on the lower-resolution WHU-TLC dataset, all 3D VFMs exhibited notable performance degradation, and the reconstructed results showed limited practical value, revealing persistent generalization challenges for current models in low-resolution scenarios. Overall, this study systematically quantifies the performance of 3D VFMs in satellite image-based 3D reconstruction, confirming their strong potential for high-resolution satellite applications and providing valuable insights for enhancing model robustness and generalization across complex urban and low-resolution environments.