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In this paper, we present a novel bottom-up salient object detection approach by exploiting the relationship between the saliency detection and the Markov absorption probability. First, we calculate a preliminary saliency map by the Markov absorption probability on a weighted graph via partial image borders as background prior. Unlike most of the existing(More)
In this paper, we propose an automatic hole-filling method, particularly for recovering missing feature curves and corners. We first extract the feature vertices around a hole of a CAD model and classify them into different feature sets. These feature sets are then automatically paired, using ordered double normals, Gaussian mapping and convex/concave(More)
This paper presents a novel mesh saliency detection approach based on manifold ranking in a descriptor space. Starting from the over-segmented patches of a mesh, we compute a descriptor vector for each patch based on Zernike coefficients, and the local distinctness of each patch by a center-surround operator. Patches with small or high local distinctness(More)
This paper proposes a consolidation method for scanned point clouds that are usually corrupted by noises, outliers, and thickness. At the beginning, we construct neighborhood of a point based on shared nearest neighbor relationship. Then, the points with few number of neighbors are regarded as outliers and removed. After that, we propose a feature-aware(More)
3D printers have become popular in recent years and enable fabrication of custom objects for home users. However, the cost of the material used in printing remains high. In this paper, we present an automatic solution to design a skin-frame structure for the purpose of reducing the material cost in printing a given 3D object. The frame structure is designed(More)
We present a global method for consistently orienting a defective raw point set with noise, non-uniformities and thin sharp features. Our method seamlessly combines two simple but effective techniques—constrained Laplacian smoothing and visibility voting—to tackle this challenge. First, we apply a Laplacian contraction to the given point cloud, which(More)
In this paper, we present a robust normal estimation algorithm based on the low-rank subspace clustering technique. The main idea is based on the observation that compared with the points around sharp features, it is relatively easier to obtain accurate normals for the points within smooth regions. The points around sharp features and smooth regions are(More)
The transforming growth factor-β (TGF-β) signaling pathway is believed to contribute to carcinoma development by increasing cell invasiveness and metastasis and inducing the epithelial-to-mesenchymal transition (EMT). Protein phosphatase PPM1A has been reported to dephosphorylate TGF-β-activated Smad2/3, thus inhibiting the TGF-β signaling pathway. In this(More)
Consistent normal orientation is challenging in the presence of noise, non-uniformities and thin sharp features. None of any existing local or global methods is capable of orienting all point cloud models consistently, and none of them offers a mechanism to rectify the inconsistent normals. In this paper, we present a new normal orientation method based on(More)
Semantic mesh segmentation and labeling is a fundamental problem in graphics. Conventional data-driven approaches usually employ a tedious offline pre-training process. Moreover, the number and especially the quality of the manually labeled examples challenge such strategies. In this paper, we develop a low-rank representation model with structure guiding(More)