Conference Agenda

This paper introduces the Appearance-guided Scaling Diffusion Model (AGDM), a novel diffusion-based framework designed to densify sparse airborne laser scanning (ALS) point clouds while preserving fine geometric detail. Traditional diffusion models for 3D upsampling, such as LiDiff and PUDM, operate solely on intrinsic 3D information and struggle to reconstruct sharp edges and continuous surfaces when input data are extremely sparse. AGDM addresses these limitations by integrating two complementary conditional priors: multi-view appearance cues and geometry-aware 3D features.

Sparse point clouds are first rendered into ten synthetic viewpoints, and a Vision Transformer extracts high-level visual embeddings that encode surface appearance and boundary structures. In parallel, a Minkowski-based encoder processes the input geometry to capture spatial continuity and local shape characteristics. A cross-attention fusion module aligns and combines these modalities, producing a unified conditioning signal that guides a scaling diffusion network during iterative denoising.

AGDM is trained and evaluated on the YUTO dataset, where dense ground-truth scenes are reconstructed from multi-mission ALS data. Experiments demonstrate that AGDM achieves superior performance across Chamfer Distance, Jensen–Shannon Divergence, F1 score, and multi-scale IoU metrics. Qualitative results further show that the model produces more uniform, edge-preserving, and structurally coherent point clouds than existing diffusion approaches.

By leveraging appearance guidance alongside geometric priors, AGDM significantly improves the fidelity and practicality of LiDAR point-cloud upsampling, offering an effective pathway for scalable and cost-efficient 3D digital-twin generation.

Accurate 3D surface reconstruction and change analysis relies on point clouds representing persistent solid surfaces and should neglect very small (< laser footprint size) and temporary objects that create outliers. Terrestrial and Permanent Laser Scanning (TLS/PLS) data often contains transient or detached points, which violate assumptions of common cloud-, mesh-, and surface-based 3D change analysis methods. Those points cause wrong correspondences and change values in multi-temporal point cloud comparison. We address this with the Scan Outlier Ratio (ScOR) filter, a LiDAR scanning and survey-aware descriptor designed to identify points unsuitable for most point cloud-based change analysis methods. ScOR compares the measured point spacing with the expected spacing, assuming the surface is locally planar and orthogonal to the incoming laser beam. ScOR works with a single scan or multiple scans acquired from the same position, enabling multi-temporal neighborhoods for filtering. Using data from natural and urban environments, we analyze ScOR across different surfaces, neighborhood sizes, temporal neighborhoods, and compare it with the Statistical Outlier Removal (SOR) algorithm. Results show that ScOR successfully removes non-surface points, while preserving surface information. In our experiments, the true positive rate exceeds 95% in all but one case, while the false positive remains below 10% throughout. With neighborhoods from subsequent and aggregated epochs, the method automatically detects and removes large temporary objects (e.g., a person). Due to its interpretability, efficiency, and range-aware design, ScOR provides an effective pre-processing method for automated and near real-time 3D surface change analysis with TLS/PLS.

Autonomous positioning and scene reconstruction are crucial to the exploration and scientific research tasks of planetary rovers. 3D Gaussian splatting (3DGS) provides a new paradigm for dense reconstruction. However, the reconstruction method that relies only on monocular images will cause scale blur and insufficient geometric consistency. These problems are more prominent in planetary scenes that lack geometric constraints and weak textures. In order to overcome these limitations, we proposed a lidar-enhanced 3DGS-SLAM pipeline. By introducing sparse lidar measurements as prior information to improve depth prediction and ensuring consistent Gaussian initialization on the physical scale. Optimize the camera poses and Gaussian parameters through differentiable rendering to achieve robust localization and photometric-geometric consistency. Experiments on the Erfoud, a planetary similarity dataset, show that our method is superior to the advanced 3DGS-based SLAM system. The ATE has reduced by more than 50%. The PSNR, SSIM, and LPIPS have all improved significantly.

LiDAR-based perception models’ performance can degrade sharply when applied to data from sensors different to those they were trained on. LiDAR super-resolution aims to enhance sparse point clouds from low-cost sensors. This can help to bridge the sensor domain gap to higher resolution LiDAR. Prior work has primarily focused on reconstruction quality metrics for super-resolution with limited evaluation of downstream perception tasks. We address this gap by conducting a systematic analysis of how super-resolution quality impacts 3D object detection performance. We evaluate detection capability through zero-shot transfer experiments on the KITTI object dataset. Four representative detectors (SECOND, PointPillars, PV-RCNN, PointRCNN) trained on high-resolution data are directly applied to super-resolved low-resolution data without fine-tuning. Results reveal a critical insight: reconstruction improvements yield vastly different detection gains across architectures. PointPillars shows minimal improvement until reaching high reconstruction quality, then performance improves significantly. In contrast, PV-RCNN exhibits steady gains throughout. The highest-quality reconstruction closes up to 86% of the performance gap and enables detection in safety-critical scenarios, including distant vehicles and small pedestrians, where lower-quality methods fail entirely. This work establishes that LiDAR super-resolution effectiveness depends on both reconstruction quality and detector architecture.

Retrieving information from point clouds for analysis and visualization has gained ever-increasing interest. A growing niche in this regard is ray queries, commonly used for image synthesis. Ray tracing is widely used in computer graphics, with a multitude of solutions based on bounding volume hierarchies. However, these solutions are rarely straightforward to integrate with raw point cloud data and geospatial analytical workflows. To overcome this, we present a novel approach to ray tracing in raw point clouds that builds upon and extends existing geospatial indices. The solution is exemplified by a fast octree implementation that supports versatile query semantics, such as neighborhood queries with constraints on k and radius for both points and rays, while offering configurable data organization schemes, including layered, fixed, and adaptive depth. The evaluation demonstrates satisfactory speed and capabilities for many scientific use cases, while simultaneously exhibiting low implementation costs, high flexibility, and simplicity in integrating ray tracing into analytical point cloud workflows.

Accuracy analysis of free elevation models TDX-EDEM, AW3D30, SRTM and ASTER GDEM-3. Determination of systematic elevation model errors by Z-shift, model tilt and systematic errors as function of X and Y. Comparison with ICESat-2 data, determination of the systematic elevation model errors by ICESat-2 ATL08 data and correcting the free elevation models. Accuracy analysis of the corrected elevation models by airborne LiDAR data.

The corrections based on the ICESat-2 data significantly improved the free elevation models.