Robust Feature Graph for Point Cloud Denoising

Point cloud is a collection of 3D coordinates that are discrete geometric samples of an object's 2D surfaces. Imperfection in the acquisition process means that point clouds are often corrupted with noise. Building on recent advances in graph signal processing, we design local algorithms for 3D point cloud denoising. Specifically, we design a signal-dependent feature graph Laplacian regularizer (SDFGLR) that assumes surface normals computed from point coordinates are piecewise smooth with respect to a signal-dependent graph Laplacian matrix. Using SDFGLR as a signal prior, we formulate an optimization problem with a general 'p-norm fidelity term that can explicitly remove only two types of additive noise: small but non-sparse noise like Gaussian (using '2 fidelity term) and large but sparser noise like Laplacian (using '1 fidelity term). To establish a linear relationship between normals and 3D point coordinates, we first perform bipartite graph approximation to divide the point cloud into two disjoint node sets (red and blue). We then optimize the red and blue nodes' coordinates alternately. For '2-norm fidelity term, we iteratively solve an unconstrained quadratic programming (QP) problem, efficiently computed using conjugate gradient with a bounded condition number to ensure numerical stability. For '1-norm fidelity term, we iteratively minimize an '1-'2 cost function using accelerated proximal gradient (APG), where a good step size is chosen via Lipschitz continuity analysis. Finally, we propose simple mean and median filters for flat patches of a given point cloud to estimate the noise variance given the noise type, which in turn is used to compute a weight parameter trading off the fidelity term and signal prior in the problem formulation. Extensive experiments show state-of-the-art denoising performance among local methods using our proposed algorithms.

In the acquisition process of 3D cultural relics, it is common to encounter noise. To facilitate the generation of high-quality 3D models, we propose an approach based on graph signal processing that combines color and geometric features to denoise the point cloud. We divide the 3D point cloud into patches based on self-similarity theory and create an appropriate underlying graph with a Markov property. The features of the vertices in the graph are represented using 3D coordinates, normal vectors, and color. We formulate the point cloud denoising problem as a maximum a posteriori (MAP) estimation problem and use a graph Laplacian regularization (GLR) prior to identifying the most probable noise-free point cloud. In the denoising process, we moderately simplify the 3D point to reduce the running time of the denoising algorithm. The experimental results demonstrate that our proposed approach outperforms five competing methods in both subjective and objective assessments. It requires fewer iterations and exhibits strong robustness, effectively removing noise from the surface of cultural relic point clouds while preserving fine-scale 3D features such as texture and ornamentation. This results in more realistic 3D representations of cultural relics.

The expansion of tunnel scale has led to a massive demand for inspections. Light Detection And Ranging (LiDAR) has been widely applied in tunnel structure inspections due to its fast data acquisition speed and strong environmental adaptability. However, raw tunnel point-cloud data contain noise point clouds, such as non-structural facilities, which affect the detection of tunnel lining structures. Methods such as point-cloud filtering and machine learning have been applied to tunnel point-cloud denoising, but these methods usually require a lot of manual data preprocessing. In order to directly denoise the tunnel point cloud without preprocessing, this study proposes a comprehensive processing method for cross-section fitting and point-cloud denoising in subway shield tunnels based on the Huber loss function. The proposed method is compared with classical fitting denoising methods such as the least-squares method and random sample consensus (RANSAC). This study is experimentally verified with 40 m long shield-tunnel point-cloud data. Experimental results show that the method proposed in this study can more accurately fit the geometric parameters of the tunnel lining structure and denoise the point-cloud data, achieving a better denoising effect. Meanwhile, since coordinate system transformations are required during the point-cloud denoising process to handle the data, manual rotations of the coordinate system can introduce errors. This study simultaneously combines the Huber loss function with principal component analysis (PCA) and proposes a three-dimensional spatial coordinate system transformation method for tunnel point-cloud data based on the characteristics of data distribution.

MaskNet++: Inlier/outlier identification for two point clouds

Point cloud denoising is a fundamental and challenging problem in geometry processing. Existing methods typically involve direct denoising of noisy input or filtering raw normals followed by point position updates. Recognizing the crucial relationship between point cloud denoising and normal filtering, we re-examine this problem from a multitask perspective and propose an end-to-end network called PCDNF for joint normal filtering-based point cloud denoising. We introduce an auxiliary normal filtering task to enhance the network's ability to remove noise while preserving geometric features more accurately. Our network incorporates two novel modules. First, we design a shape-aware selector to improve noise removal performance by constructing latent tangent space representations for specific points, taking into account learned point and normal features as well as geometric priors. Second, we develop a feature refinement module to fuse point and normal features, capitalizing on the strengths of point features in describing geometric details and normal features in representing geometric structures, such as sharp edges and corners. This combination overcomes the limitations of each feature type and better recovers geometric information. Extensive evaluations, comparisons, and ablation studies demonstrate that the proposed method outperforms state-of-the-art approaches in both point cloud denoising and normal filtering.

An increased interest in immersive applications has drawn attention to\nemerging 3D imaging representation formats, notably light fields and point\nclouds (PCs). Nowadays, PCs are one of the most popular 3D media formats, due\nto recent developments in PC acquisition, namely with new depth sensors and\nsignal processing algorithms. To obtain high fidelity 3D representations of\nvisual scenes a huge amount of PC data is typically acquired, which demands\nefficient compression solutions. As in 2D media formats, the final perceived PC\nquality plays an important role in the overall user experience and, thus,\nobjective metrics capable to measure the PC quality in a reliable way are\nessential. In this context, this paper proposes and evaluates a set of\nobjective quality metrics for the geometry component of PC data, which plays a\nvery important role in the final perceived quality. Based on the popular PSNR\nPC geometry quality metric, the novel improved PSNR-based metrics are proposed\nby exploiting the intrinsic PC characteristics and the rendering process that\nmust occur before visualization. The experimental results show the superiority\nof the best-proposed metrics over the state-of-the-art, obtaining an\nimprovement of up to 32% in the Pearson correlation coefficient.\n

As 3D scanning devices and depth sensors advance, point clouds have attracted increasing attention as a format for 3D object representation, with applications in various fields such as tele-presence, navigation and heritage reconstruction. However, point clouds usually exhibit holes of missing data, mainly due to the limitation of acquisition techniques and complicated structure. Hence, we propose an efficient point cloud inpainting method, leveraging on graph signal processing and based on the observation of non-local self-similarity in point clouds. Specifically, we split a point cloud into fixed-size cubes as the processing unit, and globally search for the most similar cube to the target cube with holes inside. The similarity metric between two cubes is defined based on the direct component and the proposed anisotropic graph total variation of normals in each cube. We then formulate the hole-filling step as an optimization problem, based on the selected most similar cube and regularized by a graph-signal smoothness prior. Experimental results show that the proposed approach outperforms three competing methods significantly, both in objective and subjective quality.

In reverse engineering, the point cloud data processing is the key, it will directly affect the quality and efficiency of the subsequent reconstruction of the surface. However, the amount of data obtained by three-dimensional laser scanning is often larger and redundant, which will increase the difficulty of the surface reconstruction, therefore, the point cloud data is needed to streamline. Depth analysis of the efficiency and accuracy of curvature grid method, a secondary streamlined algorithm is proposed, which take use of non-uniform grid streamline law based on octree to streamline to the point cloud, and re-use curvature streamline method based on B-spline surface fitting to streamline it. This method, whose streamline ratio is relatively high, can effectively retain the feature point cloud data, providing a great convenience for the follow-up of reverse engineering.

Target recognizing of unmanned aerial vehicle (UAV) based on image processing take the advantage of 2D information containing in the image for identifying the target. Compare to single UAV with electrical optical tracking system (EOTS), multi-UAV with EOTS is able to take a group of image focused on the suspected target from multiple view point. Benefit from matching each couple of image in this group, points set constituted by matched feature points implicates the depth of each point. Coordinate of target feature points could be computing from depth of feature points. This depth information makes up a cloud of points and reconstructed an exclusive 3D model to recognizing system. Considering the target recognizing do not require precise target model, the cloud of feature points was regrouped into n subsets and reconstructed to a semi-3D model. Casting these subsets in a Cartesian coordinate and applying these projections in convolutional neural networks (CNN) respectively, the integrated output of networks is the improved result of recognizing.

Large-scale 3D-scanned point clouds enable the accurate and easy recording of complex 3D objects in the real world. The acquired point clouds often describe both the surficial and internal 3D structure of the scanned objects. The recently proposed edge-highlighted transparent visualization method is effective for recognizing the whole 3D structure of such point clouds. This visualization utilizes the degree of opacity for highlighting edges of the 3D-scanned objects, and it realizes clear transparent viewing of the entire 3D structures. However, for 3D-scanned point clouds, the quality of any edge-highlighting visualization depends on the distribution of the extracted edge points. Insufficient density, sparseness, or partial defects in the edge points can lead to unclear edge visualization. Therefore, in this paper, we propose a deep learning-based upsampling method focusing on the edge regions of 3D-scanned point clouds to generate more edge points during the 3D-edge upsampling task. The proposed upsampling network dramatically improves the point-distributional density, uniformity, and connectivity in the edge regions. The results on synthetic and scanned edge data show that our method can improve the percentage of edge points more than 15% compared to the existing point cloud upsampling network. Our upsampling network works well for both sharp and soft edges. A combined use with a noise-eliminating filter also works well. We demonstrate the effectiveness of our upsampling network by applying it to various real 3D-scanned point clouds. We also prove that the improved edge point distribution can improve the visibility of the edge-highlighted transparent visualization of complex 3D-scanned objects.

Point clouds obtained from 3D scanners are often noisy and cannot be directly used for subsequent high-level tasks. In this article, we propose a novel point cloud optimization method capable of denoising and homogenizing point clouds. Our idea is based on the assumption that the noise is generally much smaller than the effective signal. We perform noise perturbation on the noisy point cloud to get a new noisy point cloud, called self-variation point cloud. The noisy point cloud and self-variation point cloud have different noise distribution, but the same point cloud distribution. We compute the potential commonality between two noisy point clouds to obtain a clean point cloud. To implement our idea, we propose a Self-Variation Capture Network (SVCNet). We perturb the point cloud features in the latent space to obtain self-variation feature vectors, and capture the commonality between two noisy feature vectors through the feature aggregation and averaging. In addition, an edge constraint module is introduced to suppress low-pass effects during denoising. Our denoising method does not take into account the noise characteristics, and can filter the drift noise located on the underlying surface, resulting in a uniform distribution of the generated point cloud. The experimental results show that our algorithm outperforms the current state-of-the-art algorithms, especially in generating more uniform point clouds. In addition, extended experiments demonstrate the potential of our algorithm for point clouds upsampling.

A point clouds denoising method based on moving least-squares is presented in this paper. The moving least-squares method has a good ability to denoise a point cloud in 2D. However, there are some serious disculties associated with the moving least-squares technique for some cases, such as varying thickness of the point set and the effects from unwanted neighboring points. Lee improves moving least-squares technique using Euclidean minimum spanning tree, region expansion and refining iteration. In this paper, we take some modification of Lee's method by using a simple method based on the correlation of the point set to find the adaptive weighting parameter, and extends it to 3D. Experimental results show that our new approach is robust and effective on denoising unorganized point clouds.

Denoising numerous large scale noise and preserving fine features simultaneously remains a challenge to point-cloud-related multiple view stereo (MVS) reconstruction approaches. The proposed algorithm reuses the sparse point cloud which is often discarded after the structure form motion (SfM) procedure in image based modeling to guide the dense point cloud denoising. Furthermore, the utilization of the octree division provides an efficient and simple denoising mechanism. Experiments show that the proposed method successfully removes the large scale noise points and presents a satisfactory denoising result with detailed information preserved. In addition, the normal of each point can be estimated fast and accurately as a by-product of the denoising algorithm.

Tomographic Synthetic Aperture Radar (TomoSAR) has become a competitive remote sensing method of three-dimensional (3-D) reconstruction over urban areas. Raw TomoSAR point clouds are inevitably noise-corrupted, which would severely obstruct the reconstruction of building structures. Whereas, data segmentation and parameter tuning are required in current methods of 3-D building reconstruction, which influences the precision and efficiency of the reconstruction process. In this paper we propose a novel method using neural networks to reconstruct 3-D building structures from TomoSAR data. By using the proposed method, the precise 3-D surface of the building structure can be retrieved quickly. The proposed method also performs commendably in point cloud denoising. More importantly, our method achieves full automation of the reconstruction process, which does not require data segmentation or complex parameter adjusting. Experiment results demonstrate the effectiveness of the proposed method.

A feature-preserving framework for point cloud denoising