Conference Agenda

This study examines PIX4D’s implementation of Gaussian Splatting for reconstructing complex geometries, with a focus on underwater photogrammetry for coral reef mapping. Unlike standard Gaussian Splatting pipelines that emphasize photorealistic rendering, our approach prioritizes high-precision geometric reconstruction, especially for thin structures and heavily occluded regions. We compare the method against conventional multi-view stereo techniques using both real underwater imagery collected in Moorea (French Polynesia) and synthetic datasets generated with the POSER underwater simulation framework.

Stand-level merchantable tree stem volume estimation in temperate forests is critical for data-driven forest management decision-making. Mobile laser scanning (MLS) has greatly improved data-collection efficiency for forest biometrics; however, automated analysis of massive, structurally complex MLS point clouds remains limited. This study presents an automated framework to estimate stand-level merchantable stem volume from backpack mobile Light Detection and Ranging (LiDAR) data. The framework comprises three stages: (1) point cloud reconstruction using the Integrated-Scan Simultaneous Trajectory Enhancement and Mapping (IS²-TEAM) method; (2) individual tree segmentation via a multistage geometric pipeline; and (3) merchantable stem volume estimation based on skeletonization-derived stem modeling. The proposed approach is evaluated on a forest-scale dataset collected in temperate natural forests in the United States. Results demonstrate operational feasibility at scale, with practical processing times and robust geometric consistency. Validation against destructively measured reference volumes shows that the proposed approach outperforms baseline quantitative structure modeling (QSM) methods, achieving a coefficient of determination (R²) of 0.97, a bias of −0.06 m³, and a root mean square error (RMSE) of 0.21 m³. The proposed framework enables reliable, automated estimation of merchantable stem volume from MLS data and supports deployment from individual-tree to forest scales with minimal manual intervention.

TRACE is a training-free framework for instance-level open-vocabulary inventory generation in 3D forensic evidence reconstruction.

Starting from multiview RGB imagery, prompt-based 2D object masks are extracted using SAM3 and associated across views via geometry-aware and appearance-aware multiview instancing. Based on COLMAP geometry and DINOv2/v3 descriptors, the proposed framework establishes globally consistent same-class object identities across the scene. The resulting global instances are then encoded with SigLIP2 to obtain language-aligned instance descriptors and subsequently lifted into a 3D Gaussian Splat representation by assigning instance-level semantics to geometrically supported Gaussian subsets. This yields an enriched 3D scene representation that jointly preserves spatial structure, object-level identity, and language-accessible semantics, thereby enabling instance-aware open-vocabulary querying in 3D.

The purpose of this study is to automatically classify water and land areas with LiDAR point clouds. After determining the average water level, the water and land surfaces were classified. Previous studies have focused on supervised classification based on land sampling or deep learning techniques using photographs. However, these classification techniques are expensive and require long calculation times. In this study, a method is proposed for the automatic classification of water and land areas without land surveys using the coordinate and reflection values of LiDAR point clouds. The bounding box method was used to detect water surface levels. The correlations between the min-box level, mean box height, and mean box reflection values of the LiDAR point data were used to determine the water surface level. The results show that the method is suitable for the fast classification of water surfaces from LiDAR point clouds. Thus, shoreline changes in large areas can be detected automatically without the need for land surveying. The proposed bounding box classification method can be applied independently of LiDAR point cloud density. The extended version of this method can also be used to detect vehicles and objects on a water surface.

Underground environments such as sewer networks are critical infrastructure whose condition directly affects public health, environmental protection, and maintenance costs. Conventional inspection workflows largely rely on monocular CCTV systems and manual video review, providing limited 3D understanding and often missing subtle or spatially complex defects. At the same time, sewer environments are characterised by challenging imaging conditions, including low illumination, specular surfaces, water films and occlusions, which further complicate reliable assessment.

In this extended abstract, we present a real-time inspection concept that combines (i) stereo camera-based SLAM for geometric mapping and pose estimation, (ii) Vision Transformer (ViT) based anomaly detection trained on the public SewerML dataset, and (iii) lightweight Gaussian Splatting modules that create local high-resolution 3D reconstructions only in the vicinity of detected defects. The system is targeted at embedded hardware, specifically an NVIDIA Jetson Nano, and is designed for deployment and evaluation in real sewer environments. The overall goal is to provide inspectors and asset managers with spatially anchored 3D visualisations of anomalies that can be integrated into digital-twin workflows for decision support and long-term monitoring.

This paper proposes a robust cross-modal matching framework for tunnel inspection, specifically designed to address the unique challenges posed by low-texture environments often encountered in tunnel linings. Traditional image-based matching techniques struggle in these environments due to the lack of distinctive surface features and limited texture variation. To overcome these challenges, the proposed method leverages the global prior knowledge of tunnel geometry. By jointly projecting LiDAR point clouds and multi-camera images onto a shared parameterized cylindrical surface, the method constructs a unified geometric space that facilitates accurate 3D–2D correspondences. This dual-projection strategy significantly improves the alignment of structural features such as segment joints, line grooves, and equipment brackets, which are critical for defect detection in tunnel inspection. The enhanced matching ability allows for more reliable multi-sensor data fusion, thereby supporting the automated analysis of tunnel defects. This framework lays a solid foundation for intelligent tunnel inspection systems, offering a powerful solution for real-time monitoring and analysis of tunnel infrastructure.