Aug 2026· Journal of Imaging· Vol 12, pp. 369· 0 citations· 38 references
Medicine
TL;DR
RG-PSR is presented, a reliability-guided enhancement framework for Poisson-family surface reconstruction from degraded 3D-imaging point clouds and is positioned as a practical reliability layer for coherent Poisson-family reconstruction rather than a universal replacement for all surface-reconstruction methods.
Abstract
Three-dimensional (3D) imaging systems, including depth cameras, LiDAR sensors, and multi-view scanning pipelines, often produce point clouds with noisy normals, outliers, sparse sampling, and non-uniform density, which can degrade downstream mesh reconstruction. Poisson surface reconstruction is lightweight and training-free, but its global implicit formulation is sensitive to unreliably oriented samples and fixed density-trimming thresholds. This paper presents RG-PSR, a reliability-guided enhancement framework for Poisson-family surface reconstruction from degraded 3D-imaging point clouds. RG-PSR estimates a deterministic per-point reliability score from local density regularity, spacing variation, and normal consistency, and propagates this score through conservative point filtering, reliability-guided normal refinement, adaptive density-reliability trimming, and structure-aware postprocessing. The main pipeline requires no manual labels, neural network training, or ground-truth meshes at inference time. Experiments on three groups of object meshes under five deterministic degradation types show that RG-PSR improves Poisson-family reconstruction under degraded inputs. Compared with fixed density-trimmed Poisson reconstruction, RG-PSR reduces the overall Chamfer-L1 from 0.0218 to 0.0172, improves F0.01 from 0.6618 to 0.6836, and reduces Artifact0.02 from 0.3090 to 0.2632. In the broader classical comparison, local triangulation methods achieve stronger point-wise accuracy, while RG-PSR yields the fewest connected components and the highest largest-component ratio. These results position RG-PSR as a practical reliability layer for coherent Poisson-family reconstruction rather than a universal replacement for all surface-reconstruction methods.
Per-point uncertainty models are important in structured-light 3D reconstruction for probabilistic registration, fusion, and quality assessment. In practice, however, point-cloud covariances are often modeled as isotropic constants or inferred from local surface geometry and therefore do not explicitly reflect the measurement process. This is a limitation in fringe projection profilometry (FPP), where phase noise propagates through calibrated reconstruction and produces strongly anisotropic 3D uncertainty. This paper presents a sensor-informed first-order method for constructing a per-point 3 x 3 covariance field from experimentally measured phase precision and calibrated phase-to-depth and phase-to-3D mappings. The formulation separates a rank-1 phase-induced covariance from an effective full-rank completion obtained by incorporating fitted lateral image-space perturbation scales. Repeated-plane experiments under fixed imaging conditions show close alignment of the dominant covariance direction with the viewing ray, and consistency between the dominant phase-induced uncertainty scale and scalar depth uncertainty. In G-ICP registration, the proposed covariance substantially improves over a constant isotropic model while providing a sensor-derived uncertainty representation complementary to conventional geometry-based covariances.
Abstract. Airborne laser scanning (ALS) point clouds are widely used for large-scale 3D scene understanding, but acquiring dense ALS data remains costly and sparse observations often exhibit incomplete vertical structures and uneven sampling. Existing diffusion-based point cloud upsampling methods have shown promise, yet conditioning only on sparse point coordinates often leads to noisy surfaces, boundary artifacts, and limited structural recovery in under-observed regions. In this work, we propose the Appearance-aware Scaling Diffusion Model (ASDM), a conditional diffusion framework for scene-level ALS point cloud upsampling that incorporates multi-view projected depth-image cues derived directly from the input point cloud. Specifically, sparse ALS scenes are rendered from multiple virtual viewpoints to generate projected depth images, which provide complementary structural information for guiding the denoising process. These projected-image features are fused with sparse point features to improve geometric fidelity and scene-level consistency. For training and evaluation, we construct realistic sparse–dense scene pairs from aerial LiDAR data derived from the YUTO Semantic dataset using a region-disjoint split over 13 survey areas. Experiments under the ×4 upsampling setting show that ASDM outperforms recent diffusion-based baselines, achieving the best overall performance in Chamfer Distance (0.5643), JSD-3D (0.6688), F1-score (75.67), and voxelized IoU at 1m and 2m resolutions. These results demonstrate that projected-image conditioning is an effective strategy for robust airborne LiDAR scene densification.
Sunghwan Yoo, Gunho Sohn· The International Archives o...· 0 citations
3D scene understanding is increasingly important in construction, yet most methods are developed on curated datasets that do not fully reflect real site sensing conditions. In many workflows, individual LiDAR scans provide rapid local updates rather than complete scene representations, producing limited surface coverage, acquisition-driven density variation, and severe imbalance between dominant planar surfaces and sparse construction elements. Because large point clouds must be downsampled, sampling resolution and point allocation directly affect the balance between geometric detail and spatial context. This study evaluates these effects under a fixed per-fragment point budget and introduces an incidence-aware sampling strategy for individual LiDAR scans. The method maps points to a geometry-normalized manifold space for voxel-based selection while preserving original Euclidean coordinates for downstream learning. It requires only point coordinates and normals and no backbone modification. Using the Site in Pieces (SIP) benchmark, experiments with Point Transformer and PointNeXt show improved resolution-averaged segmentation performance, especially for non-planar elements and ladders, while reducing sensitivity to sampling resolution. The results show that acquisition-aware sampling can provide a more stable geometric representation and should be treated as an active component of individual-scan 3D segmentation rather than generic preprocessing.
Seongyong Kim, Jingdao Chen, Y. K. Cho· 0 citations
A semantic-guided 3D Gaussian splatting (3DGS) framework tailored to sparse-view industrial reconstruction was introduced, enabling robust reconstruction from limited viewpoints and offers a practical geometric foundation for automated inspection and remote equipment monitoring.
Boyang Li, Tian-Han Gao, Zuan Gu et al.· Visual Computing for Industr...· 0 citations
A 3D-GS-based framework that attaches a simplified Disney Bidirectional Reflectance Distribution Function (BRDF) and differentiable physically based rendering (PBR) shader to each surface Gaussian for interpretable material attributes delivers higher geometric accuracy, improved material realism, and real-time rendering performance suitable for applications such as Simultaneous Localization and Mapping (SLAM) and relighting.
Wei-Chen Xu, W. Wan, M. Peng· International Conference on...· 0 citations
Existing LiDAR super-resolution methods primarily aim to increase point cloud density or improve coordinate reconstruction accuracy. However, they tend to introduce blurred edges and distorted planar surfaces during reconstruction, making it difficult to preserve the consistency of local scene geometry. To address this issue, this paper proposes a structural consistency-aware LiDAR super-resolution method that aims to preserve the local geometric relationships of the reconstructed point cloud with respect to the ground-truth point cloud in edge and planar regions. Specifically, complementary observations from adjacent frames are first fused using multi-scale dilated convolutions. An anisotropic Swin Transformer and a Coordinate-Aware Structure Enhancement (CASE) module are then employed to accommodate the horizontally dense and vertically sparse sampling pattern of LiDAR, strengthen long-range geometric modeling, and reduce the loss of critical structural information. During training, a local curvature-based structural consistency loss is designed to separately constrain edge sharpness and planar smoothness. During inference, prediction uncertainty and point cloud height are combined to adaptively remove low-confidence points, further improving the geometric reliability of the reconstructed point cloud. Experiments on the KITTI dataset show that the proposed method achieves an MAE of 0.4916 and an IoU of 0.4633, outperforming the representative comparison methods on both metrics. When the reconstructed point clouds are applied to A-LOAM, the average RTE and RRE values are reduced by 34.6% and 31.2%, respectively. In addition, experiments on the self-collected CSU-SLAM dataset provide preliminary evidence of the applicability of the proposed method to indoor and outdoor scenes under a different LiDAR configuration.
Jun Zeng, Chun-Qiu Xia, Hong-Wei Zhang et al.· Photonics· 0 citations
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