Urban building reconstruction is an important step for urban digitization and realisticvisualization. In this paper, we propose a novel automatic method to recover urban building geometry from 3D point clouds. The proposed method is suitable for buildings composed of planar polygons and aligned with the gravity direction, which are quite common in the city. Our key observation is that the building shapes are usually piecewise constant along the gravity direction and determined by several dominant shapes. Based on this observation, we formulate building reconstruction as an energy minimization problem under the Markov Random Field (MRF) framework. Specifically, point clouds are first cutinto a sequence of slices along the gravity direction. Then, floorplans are reconstructed by extracting boundaries of these slices, among which dominant floorplans are extracted and propagated to other floors via MRF. To guarantee correct propagation, a new distance measurement for floorplans is designed, which first encodes floorplans into strings and then calculates distances between their corresponding strings. Additionally, an image based editing method is also proposed to recover detailed window structures. Experimental results on both synthetic and real data sets have validated the effectiveness of our method.

Sparse representation-(or sparse coding)-based classification has been successfully applied to face recognition. However, it can become problematic in the presence of illumination variations or occlusions. In this paper, we propose a Manifold Regularized Local Sparse Representation (MRLSR) model to address such difficulties. The key idea behind the MRLSR method is that all coding vectors in sparse representation should be group sparse, which means holding the two properties of both individual sparsity and local similarity. As a consequence, the face recognition rate can be considerably improved. The MRLSR model is optimized by the modified homotopy algorithm, which keeps stable under different choices of the weighting parameter. Extensive experiments are performed on various face databases, which contain illumination variations and occlusions. We show that the proposed method outperforms the state-of-the-art approaches and provides the highest recognition rate.

In this paper, we propose a method for joint 2D segmentation and 2D-3D pose tracking. First, we define a novel energy functional which considers the discrimination between statistical appearance models and the coherence among neighboring pixels simultaneously. And then, a particle filter-like stochastic optimization technique is adopted to solve the energy functional, so that a preferable initial value can be provided for the subsequent damped Newton optimization method. Furthermore, an occlusion-aware updating strategy is utilized for appearance models, which can easily increase the foreground learning rate. As a result, our method is more suitable for the video sequences with occlusion. Experimental results highlight excellent performance on challenging synthetic and real-world sequences as compared with the state-of-the-art approaches.

In this paper, we present a fast image upsampling method within a two-scale framework to ensure the sharp construction of upsampled image for both large-scale edges and small-scale structures. In our approach, the low-frequency image is recovered via a novel sharpness preserving interpolation technique based on a well-constructed displacement field, which is estimated by a cross-resolution sharpness preserving model. Within this model, the distances of pixels on edges are preserved, which enables the recovery of sharp edges in the high-resolution result. Likewise, local high-frequency structures are reconstructed via a sharpness preserving reconstruction algorithm. Extensive experiments show that our method outperforms current state-of-the-art approaches, based on quantitative and qualitative evaluations, as well as perceptual evaluation by a user study. Moreover, our approach is very fast so as to be practical for real applications.

Super-resolution from a single image plays an important role in many computer vision systems. However, it is still a challenging task, especially in preserving local edge structures. To construct high-resolution images while preserving the sharp edges, an effective edge-directed super-resolution method is presented in this paper. An adaptive self-interpolation algorithm is first proposed to estimate a sharp high-resolution gradient field directly from the input low-resolution image. The obtained high-resolution gradient is then regarded as a gradient constraint or an edge-preserving constraint to reconstruct the high-resolution image. Extensive results have shown both qualitatively and quantitatively that the proposed method can produce convincing super-resolution images containing complex and sharp features, as compared with the other state-of-the-art super-resolution algorithms.

Object tracking plays an important role in many intelligent transportation systems. Unfortunately, it remains a challenging task due to factors such as occlusion and target-appearance variation. In this paper, we present a new tracking algorithm to tackle the difficulties caused by these two factors. First, considering the target-appearance variation, we introduce the local-background-weighted histogram (LBWH) to describe the target. In our LBWH, the local background is treated as the context of the target representation. Compared with traditional descriptors, the LBWH is more robust to the variability or the clutter of the potential background. Second, to deal with the occlusion case, a new forward-backward mean-shift (FBMS) algorithm is proposed by incorporating a forward-backward evaluation scheme, in which the tracking result is evaluated by the forward-backward error. Extensive experiments on various scenarios have demonstrated that our tracking algorithm outperforms the state-of-the-art approaches in tracking accuracy.

In this paper, we propose a novel framework to construct dense and high-quality consistent correspondence between non-rigid surfaces. Our correspondence framework exploits “Dual Shape-DNA” (dual Laplace-Beltrami spectral embedding) to capture global characteristics of objects, and converts two originally different and complex shapes into two similar and simple shapes to facilitate the correspondence. Since our method avoids the computation of geodesic distances, it is robust to local topology changes. By exploiting the excellent properties of the dual domain, our dual spectral framework can robustly construct Laplace-Beltrami embeddings on highly non-regular 3D meshes. After performing initial non-rigid matching in the dual Laplace-Beltrami spectral domain, we return 3D spatial domain and apply a shape-preserving non-rigid deformation to produce the final dense consistent correspondence. We show that our framework is suitable for non-rigid consistent correspondence, and the high-quality correspondence results are achieved.

In this paper, we propose a novel partwise framework for cross-parameterization between 3D mesh models. Unlike most existing methods that use regular parameterization domains, our framework uses nonregular approximation domains to build the cross-parameterization. Once the nonregular approximation domains are constructed for 3D models, different (and complex) input shapes are transformed into similar (and simple) shapes, thus facilitating the cross-parameterization process. Specifically, a novel nonregular domain, the convex hull, is adopted to build shape correspondence. We first construct convex hulls for each part of the segmented model, and then adopt our convex-hull cross-parameterization method to generate compatible meshes. Our method exploits properties of the convex hull, e.g., good approximation ability and linear convex representation for interior vertices. After building an initial cross-parameterization via convex-hull domains, we use compatible remeshing algorithms to achieve an accurate approximation of the target geometry and to ensure a complete surface matching. Experimental results show that the compatible meshes constructed are well suited for shape blending and other geometric applications.

In this paper, based on manifold harmonics, we propose a novel framework for 3D shape similarity comparison and partial matching. First, we propose a novel symmetric mean-value representation to robustly construct high-quality manifold harmonic bases on nonuniform-sampling meshes. Then, based on the manifold harmonic bases constructed, a novel shape descriptor is presented to capture both of global and local features of 3D shape. This feature descriptor is isometry-invariant, i.e., invariant to rigid-body transformations and non-rigid bending. After characterizing 3D models with the shape features, we perform 3D retrieval with a up-to-date discriminative kernel. This kernel is a dimension-free approach to quantifying the similarity between two unordered feature-sets, thus especially suitable for our high-dimensional feature data. Experimental results show that our framework can be effectively used for both comprehensive comparison and partial matching among non-rigid 3D shapes.

In this paper, based on active appearance model (AAM), we present an easy-to-use framework for facial image composition, which can automatically exchange the source image's face or facial features onto the target image. The manual interaction is simple and the user only needs to input semantic information of ROI (region of interest) to be exchanged, such as 'face' or 'eyes'. Our framework mainly consists of two steps: model fitting and component compositing. Model fitting is designed to interpret each input image and obtain a synthesized model face of the image. Then by using component compositing, visual pleasing result is generated by solving Poisson equation with the boundary condition, produced automatically from model fitting. Furthermore, we propose a solution for eliminating the artifacts when part of the target face is occluded by hair, glasses, etc. The visually satisfactory results demonstrate the effectiveness of our facial image composition system.

We propose a novel framework for consistent correspondence between arbitrary manifold meshes. Different from most existing methods, our approach directly maps the connectivity of the source mesh onto the target mesh without needing to segment input meshes, thus effectively avoids dealing with unstable extreme conditions (e.g. complex boundaries or high genus). In this paper, firstly, a novel mean-value Laplacian fitting scheme is proposed, which aims at computing a shape-preserving (conformal) correspondence directly in 3D-to-3D space, efficiently avoiding local optimum caused by the nearest-point search, and achieving good results even with only a few marker points. Secondly, we introduce a vertex relocation and projection approach, which refines the initial fitting result in the way of local conformity. Each vertex of the initial result is gradually projected onto the target model's surface to ensure a complete surface match. Furthermore, we provide a fast and effective approach to automatically detect critic points in the context of consistent correspondence. By fitting these critic points that capture the important features of the target mesh, the output compatible mesh matches the target mesh's profiles quite well. Compared with previous approaches, our scheme is robust, fast, and convenient, thus suitable for common applications.