Qunsheng Peng

Fast Non-Local Algorithm for Image Denoising

For the non-local denoising approach presented by Buades et al., remarkable denoising results are obtained at high expense of computational cost. In this paper, a new algorithm that reduces the computational cost for calculating the similarity of neighborhood windows is proposed. We first introduce an approximate measure about the similarity of neighborhood windows, then we use an efficient summed square image (SSI) scheme and fast Fourier transform (FFT) to accelerate the calculation of this measure. Our algorithm is about fifty times faster than the original non-local algorithm both theoretically and experimentally, yet produces comparable results in terms of mean-squared error (MSE) and perceptual image quality.

A robust and fast non-local means algorithm for image denoising

In the paper, we propose a robust and fast image denoising method. The approach integrates both Non-Local means algorithm and Laplacian Pyramid. Given an image to be denoised, we first decompose it into Laplacian pyramid. Exploiting the redundancy property of Laplacian pyramid, we then perform non-local means on every level image of Laplacian pyramid. Essentially, we use the similarity of image features in Laplacian pyramid to act as weight to denoise image. Since the features extracted in Laplacian pyramid are localized in spatial position and scale, they are much more able to describe image, and computing the similarity between them is more reasonable and more robust. Also, based on the efficient Summed Square Image (SSI) scheme and Fast Fourier Transform (FFT), we present an accelerating algorithm to break the bottleneck of non-local means algorithm — similarity computation of compare windows. After speedup, our algorithm is fifty times faster than original non-local means algorithm. Experiments demonstrated the effectiveness of our algorithm.

Real-time soft shadows in dynamic scenes using spherical harmonic exponentiation

Previous methods for soft shadows numerically integrate over many light directions at each receiver point, testing blocker visibility in each direction. We introduce a method for real-time soft shadows in dynamic scenes illuminated by large, low-frequency light sources where such integration is impractical. Our method operates on vectors representing low-frequency visibility of blockers in the spherical harmonic basis. Blocking geometry is modeled as a set of spheres; relatively few spheres capture the low-frequency blocking effect of complicated geometry. At each receiver point, we compute the product of visibility vectors for these blocker spheres as seen from the point. Instead of computing an expensive SH product per blocker as in previous work, we perform inexpensive vector sums to accumulate the log of blocker visibility. SH exponentiation then yields the product visibility vector over all blockers. We show how the SH exponentiation required can be approximated accurately and efficiently for low-order SH, accelerating previous CPU-based methods by a factor of 10 or more, depending on blocker complexity, and allowing real-time GPU implementation.

Real-time voxelization for complex polygonal models

In this paper, we present an efficient voxelization algorithm for complex polygonal models by exploiting newest programmable graphics hardware. We first convert the model into three discrete voxel spaces according to its surface orientation. The resultant voxels are encoded as 2D textures and stored in three intermediate sheet buffers called directional sheet buffers. These buffers are finally synthesized into one worksheet, which records the volumetric representation of the target. The whole algorithm traverses the geometric model only once and is accomplished entirely in GPU (graphics processing unit), achieving real-time frame rate for models with up to 2 million triangles.

Gradient domain editing of deforming mesh sequences

Many graphics applications, including computer games and 3D animated films, make heavy use of deforming mesh sequences. In this paper, we generalize gradient domain editing to deforming mesh sequences. Our framework is keyframe based. Given sparse and irregularly distributed constraints at unevenly spaced keyframes, our solution first adjusts the meshes at the keyframes to satisfy these constraints, and then smoothly propagate the constraints and deformations at keyframes to the whole sequence to generate new deforming mesh sequence. To achieve convenient keyframe editing, we have developed an efficient alternating least-squares method. It harnesses the power of subspace deformation and two-pass linear methods to achieve high-quality deformations. We have also developed an effective algorithm to define boundary conditions for all frames using handle trajectory editing. Our deforming mesh editing framework has been successfully applied to a number of editing scenarios with increasing complexity, including footprint editing, path editing, temporal filtering, handle-based deformation mixing, and spacetime morphing.

Topology cuts: A novel min-cut/max-flow algorithm for topology preserving segmentation in N-D images

Topology is an important prior in many image segmentation tasks. In this paper, we design and implement a novel graph-based min-cut/max-flow algorithm that incorporates topology priors as global constraints. We show that the optimization of the energy function we consider here is NP-hard. However, our algorithm is guaranteed to find an approximate solution that conforms to the initialization, which is a desirable property in many applications since the globally optimum solution does not consider any initialization information. The key innovation of our algorithm is the organization of the search for maximum flow in a way that allows consideration of topology constraints. In order to achieve this, we introduce a label attribute for each node to explicitly handle the topology constraints, and we use a distance map to keep track of those nodes that are closest to the boundary. We employ the bucket priority queue data structure that records nodes of equal distance and we efficiently extract the node with minimal distance value. Our methodology of embedding distance functions in a graph-based algorithm is general and can also account for other geometric priors. Experimental results show that our algorithm can efficiently handle segmentation cases that are challenging for graph-cut algorithms. Furthermore, our algorithm is a natural choice for problems with rich topology priors such as object tracking.

Easy Matting ‐ A Stroke Based Approach for Continuous Image Matting

We propose an iterative energy minimization framework for interactive image matting. Our approach is easy in the sense that it is fast and requires only few user‐specified strokes for marking the foreground and background. Beginning with the known region, we model the unknown region as a Markov Random Field (MRF) and formulate its energy in each iteration as the combination of one data term and one smoothness term. By automatically adjusting the weights of both terms during the iterations, the first‐order continuous and feature‐preserving result is rapidly obtained with several iterations. The energy optimization can be further performed in selected local regions for refined results. We demonstrate that our energy‐driven scheme can be extended to video matting, with which the spatio‐temporal smoothness is faithfully preserved. We show that the proposed approach outperforms previous methods in terms of both the quality and performance for quite challenging examples.

MIVAS: A Multi-Modal Immersive Virtual Assembly System

Evaluation and planning of assembly processes in virtual environments have become an active research area in engineering community. However, planning of complex assemblies in virtual environments, especially large-scale virtual environments, is still hindered by limitations like unnatural user interaction, insufficient frame rates, and deficiencies in processing of assembly constraints. In this paper, we present MIVAS, a Multi-modal Immersive Virtual Assembly System. By viewing the virtual assembly system as a finite state machine, we incorporate tracked devices, force feedback dataglove, voice commands, human sounds, fully immersive 4-sided CAVE, together with optimization techniques for both complex assembly models and assembly operations to provide for engineers an intuitive and natural way of assembly evaluation and planning. Testing scenarios on disassembling different components of an intelligent hydraulic excavator are described. Special attention is paid upon such technical issues as interface between CAD packages and the CAVE virtual environment, natural and intuitive user interaction including realistic virtual hand interaction and force feedback, intelligent navigation for assembly operations, and real-time display of complex assemblies.Copyright

Robust Feature-Preserving Mesh Denoising Based on Consistent Subneighborhoods

In this paper, we introduce a feature-preserving denoising algorithm. It is built on the premise that the underlying surface of a noisy mesh is piecewise smooth, and a sharp feature lies on the intersection of multiple smooth surface regions. A vertex close to a sharp feature is likely to have a neighborhood that includes distinct smooth segments. By defining the consistent subneighborhood as the segment whose geometry and normal orientation most consistent with those of the vertex, we can completely remove the influence from neighbors lying on other segments during denoising. Our method identifies piecewise smooth subneighborhoods using a robust density-based clustering algorithm based on shared nearest neighbors. In our method, we obtain an initial estimate of vertex normals and curvature tensors by robustly fitting a local quadric model. An anisotropic filter based on optimal estimation theory is further applied to smooth the normal field and the curvature tensor field. This is followed by second-order bilateral filtering, which better preserves curvature details and alleviates volume shrinkage during denoising. The support of these filters is defined by the consistent subneighborhood of a vertex. We have applied this algorithm to both generic and CAD models, and sharp features, such as edges and corners, are very well preserved.

A new free-form deformation through the control of parametric surfaces

Abstract A new free-form deformation method is presented in this paper. The deformation of an object is achieved by attaching it to two parametric surfaces, namely the shape surface S (u, ν) and the height surface H (u, ν) . A control point or vertex of the object is projected onto the shape surface along its normal and a correspondence between the point and its projection on the shape surface is established. The point is then embedded into the parametric space defined by the shape surface. By regarding the height surface as a displacement function, the directed distance from the sample point to its projection on the shape surface can be further adjusted. The proposed method is independent of the representation of the underlying object. Experimental results show that the method is intuitive, easy to control and fast to run.