Mingqiang Wei

Bi-Normal Filtering for Mesh Denoising

Most mesh denoising techniques utilize only either the facet normal field or the vertex normal field of a mesh surface. The two normal fields, though contain some redundant geometry information of the same model, can provide additional information that the other field lacks. Thus, considering only one normal field is likely to overlook some geometric features. In this paper, we take advantage of the piecewise consistent property of the two normal fields and propose an effective framework in which they are filtered and integrated using a novel method to guide the denoising process. Our key observation is that, decomposing the inconsistent field at challenging regions into multiple piecewise consistent fields makes the two fields complementary to each other and produces better results. Our approach consists of three steps: vertex classification, bi-normal filtering, and vertex position update. The classification step allows us to filter the two fields on a piecewise smooth surface rather than a surface that is smooth everywhere. Based on the piecewise consistence of the two normal fields, we filtered them using a piecewise smooth region clustering strategy. To benefit from the bi-normal filtering, we design a quadratic optimization algorithm for vertex position update. Experimental results on synthetic and real data show that our algorithm achieves higher quality results than current approaches on surfaces with multifarious geometric features and irregular surface sampling.

Scale-adaptive surface modeling of vascular structures

BackgroundThe effective geometric modeling of vascular structures is crucial for diagnosis, therapy planning and medical education. These applications require good balance with respect to surface smoothness, surface accuracy, triangle quality and surface size.MethodsOur method first extracts the vascular boundary voxels from the segmentation result, and utilizes these voxels to build a three-dimensional (3D) point cloud whose normal vectors are estimated via covariance analysis. Then a 3D implicit indicator function is computed from the oriented 3D point cloud by solving a Poisson equation. Finally the vessel surface is generated by a proposed adaptive polygonization algorithm for explicit 3D visualization.ResultsExperiments carried out on several typical vascular structures demonstrate that the presented method yields both a smooth morphologically correct and a topologically preserved two-manifold surface, which is scale-adaptive to the local curvature of the surface. Furthermore, the presented method produces fewer and better-shaped triangles with satisfactory surface quality and accuracy.ConclusionsCompared to other state-of-the-art approaches, our method reaches good balance in terms of smoothness, accuracy, triangle quality and surface size. The vessel surfaces produced by our method are suitable for applications such as computational fluid dynamics simulations and real-time virtual interventional surgery.

Coarse-to-Fine Normal Filtering for Feature-Preserving Mesh Denoising Based on Isotropic Subneighborhoods

State-of-the-art normal filters usually denoise each face normal using its entire anisotropic neighborhood. However, enforcing these filters indiscriminately on the anisotropic neighborhood will lead to feature blurring, especially in challenging regions with shallow features. We develop a novel mesh denoising framework which can effectively preserve features with various sizes. Our idea is inspired by the observation that the underlying surface of a noisy mesh is piecewise smooth. In this regard, it is more desirable that we denoise each face normal within its piecewise smooth region (we call such a region as an isotropic subneighborhood) instead of using the anisotropic neighborhood. To achieve this, we first classify mesh faces into several types using a face normal tensor voting and then perform a normal filter to obtain a denoised coarse normal field. Based on the results of normal classification and the denoised coarse normal field, we segment the anisotropic neighborhood of every feature face into a number of isotropic subneighborhoods via local spectral clustering. Thus face normal filtering can be performed again on the isotropic subneighborhoods and produce a more accurate normal field. Extensive tests on various models demonstrate that our method can achieve better performance than state-of-the-art normal filters, especially in challenging regions with features.

Robust curve skeleton extraction for vascular structures

Extracting curve skeletons for vascular structures is vital for many medical applications. However, most of existing curve skeleton extraction methods are either too complicated or not robust to be applied directly on vascular meshes. In this paper, we present a simple and robust three-step approach for one-dimensional curve skeleton extraction for vascular models. Firstly, the given vascular mesh is iteratively contracted until it is thin enough. Then the contracted mesh is further subdivided. Thereafter our approach proceeds over the point cloud domain yielded by the vertices of the subdivided mesh. Secondly, the joint and branch points of the model are detected. Finally, a skeleton growing procedure is proposed to generate the curve skeleton. Experimental results show that our approach is robust for vascular structures of any topology, e.g. with or without loops or with nearby structures. Additional experiments demonstrate that our approach can be extended to handle other common shapes.

A robust and fast approach to simulating the behavior of guidewire in vascular interventional radiology

Interventional radiology (IR) is widely used in the treatment of cardiovascular disease. The manipulation of the guidewire and catheter is an essential skill in IR procedure. Computer-based training simulators can provide solutions to overcome many drawbacks of the traditional apprenticeship training during the procedure. In this paper, a physically-based approach to simulating the behavior of the guidewire is presented. Our approach models the guidewire as thin flexible elastic rods with different resolutions which are dynamically adaptive to the curvature of the vessel. More material characteristics of this deformable material are integrated into our discrete model to realistically simulate the behavior of the wire. A force correction strategy is proposed to adjust the elastic force to avoid endless collision detections. Several experimental tests on our simulator are given to demonstrate the effectiveness of our approach.

Non-local sparse and low-rank regularization for structure-preserving image smoothing

This paper presents a new image smoothing method that better preserves prominent structures. Our method is inspired by the recent non‐local image processing techniques on the patch grouping and filtering. Overall, it has three major contributions over previous works. First, we employ the diffusion map as the guidance image to improve the accuracy of patch similarity estimation using the region covariance descriptor. Second, we model structure‐preserving image smoothing as a low‐rank matrix recovery problem, aiming at effectively filtering the texture information in similar patches. Lastly, we devise an objective function, namely the weighted robust principle component analysis (WRPCA), by regularizing the low rank with the weighted nuclear norm and sparsity pursuit with L1 norm, and solve this non‐convex WRPCA optimization problem by adopting the alternative direction method of multipliers (ADMM) technique. We experiment our method with a wide variety of images and compare it against several state‐of‐the‐art methods. The results show that our method achieves better structure preservation and texture suppression as compared to other methods. We also show the applicability of our method on several image processing tasks such as edge detection, texture enhancement and seam carving.

Automatic modeling of urban facades from raw LiDAR point data

Modeling of urban facades from raw LiDAR point data remains active due to its challenging nature. In this paper, we propose an automatic yet robust 3D modeling approach for urban facades with raw LiDAR point clouds. The key observation is that building facades often exhibit repetitions and regularities. We hereby formulate repetition detection as an energy optimization problem with a global energy function balancing geometric errors, regularity and complexity of facade structures. As a result, repetitive structures are extracted robustly even in the presence of noise and missing data. By registering repetitive structures, missing regions are completed and thus the associated point data of structures are well consolidated. Subsequently, we detect the potential design intents (i.e., geometric constraints) within structures and perform constrained fitting to obtain the precise structure models. Furthermore, we apply structure alignment optimization to enforce position regularities and employ repetitions to infer missing structures. We demonstrate how the quality of raw LiDAR data can be improved by exploiting data redundancy, and discovering high level structural information (regularity and symmetry). We evaluate our modeling method on a variety of raw LiDAR scans to verify its robustness and effectiveness.

Effective mesh smoothing for haptic rendering in medical applications

Mesh models can be extracted from medical imaging data. However some methods (e.g., CT) may suffer from severe artifacts (e.g., staircases, noises) in current clinical routine. As a consequence, haptic systems, when using these influenced mesh models, will become unstable. To tackle this problem, in this paper we propose an effective medical-oriented smoothing algorithm focusing on haptic rendering. Our algorithm mainly consists of two stages, namely vertex re-sampling and surface fitting. The first stage is adopted to eliminate staircases while the second can obtain the underlying surface by least square fitting method. Experiments on various medical imaging data present the efficacy of our methodology, which can achieve higher quality results than previous approaches regarding both surface smoothness and surface accuracy. And the final results on haptic applications further show this proposed technique is suitable for medical surgery simulations.