vMF Distribution Research Articles

Realtime Rendering Glossy to Glossy Reflections in Screen Space

Glossy to glossy reflections are lights bounced between glossy surfaces. Such directional light transports are important for humans to perceive glossy materials, but difficult to simulate. This paper proposes a new method for rendering screen-space glossy to glossy reflections in realtime. We use spherical von Mises-Fisher vMF distributions to model glossy BRDFs at surfaces, and employ screen space directional occlusion SSDO rendering framework to trace indirect light transports bounced in the screen space. As our main contributions, we derive a new parameterization of vMF distribution so as to convert the non-linear fit of multiple vMF distributions into a linear sum in the new space. Then, we present a new linear filtering technique to build MIP-maps on glossy BRDFs, which allows us to create filtered radiance transfer functions at runtime, and efficiently estimate indirect glossy to glossy reflections. We demonstrate our method in a realtime application for rendering scenes with dynamic glossy objects. Compared with screen space directional occlusion, our approach only requires one extra texture and has a negligible overhead, 3% ~ 6% loss at frame rate, but enables glossy to glossy reflections.

Minimum message length estimation of mixtures of multivariate Gaussian and von Mises-Fisher distributions

Mixture modelling involves explaining some observed evidence using a combination of probability distributions. The crux of the problem is the inference of an optimal number of mixture components and their corresponding parameters. This paper discusses unsupervised learning of mixture models using the Bayesian Minimum Message Length (MML) criterion. To demonstrate the effectiveness of search and inference of mixture parameters using the proposed approach, we select two key probability distributions, each handling fundamentally different types of data: the multivariate Gaussian distribution to address mixture modelling of data distributed in Euclidean space, and the multivariate von Mises-Fisher (vMF) distribution to address mixture modelling of directional data distributed on a unit hypersphere. The key contributions of this paper, in addition to the general search and inference methodology, include the derivation of MML expressions for encoding the data using multivariate Gaussian and von Mises-Fisher distributions, and the analytical derivation of the MML estimates of the parameters of the two distributions. Our approach is tested on simulated and real world data sets. For instance, we infer vMF mixtures that concisely explain experimentally determined three-dimensional protein conformations, providing an effective null model description of protein structures that is central to many inference problems in structural bioinformatics. The experimental results demonstrate that the performance of our proposed search and inference method along with the encoding schemes improve on the state of the art mixture modelling techniques.

A Remark on Regularity Criterion for the Navier-Stokes Equations in a Bounded Domain of ℝ N

T-SNE is a well-known approach to embedding high-dimensional data and has been widely used in data visualization. The basic assumption of t-SNE is that the data are non-constrained in the Euclidean space and the local proximity can be modelled by Gaussian distributions. This assumption does not hold for a wide range of data types in practical applications, for instance spherical data for which the local proximity is better modelled by the von Mises-Fisher (vMF) distribution instead of the Gaussian. This paper presents a vMF-SNE embedding algorithm to embed spherical data. An iterative process is derived to produce an efficient embedding. The results on a simulation data set demonstrated that vMF-SNE produces better embeddings than t-SNE for spherical data.

Bayesian Estimation of the von-Mises Fisher Mixture Model with Variational Inference

This paper addresses the Bayesian estimation of the von-Mises Fisher (vMF) mixture model with variational inference (VI). The learning task in VI consists of optimization of the variational posterior distribution. However, the exact solution by VI does not lead to an analytically tractable solution due to the evaluation of intractable moments involving functional forms of the Bessel function in their arguments. To derive a closed-form solution, we further lower bound the evidence lower bound where the bound is tight at one point in the parameter distribution. While having the value of the bound guaranteed to increase during maximization, we derive an analytically tractable approximation to the posterior distribution which has the same functional form as the assigned prior distribution. The proposed algorithm requires no iterative numerical calculation in the re-estimation procedure, and it can potentially determine the model complexity and avoid the over-fitting problem associated with conventional approaches based on the expectation maximization. Moreover, we derive an analytically tractable approximation to the predictive density of the Bayesian mixture model of vMF distributions. The performance of the proposed approach is verified by experiments with both synthetic and real data.

High-order parameter approximation for von Mises–Fisher distributions

This paper concerns the issue of the maximum-likelihood estimation (MLE) for the concentration parameters of the von Mises–Fisher (vMF) distributions, which are crucial to directional data analysis. In particular, we study the numerical approximation approach for solving the implicit nonlinear equation arising from building the MLE of the concentration parameter κ of vMF distributions. In addition, we address the implementation of Is(x), the modified Bessel function of the first kind, which is the most time-consuming and fundamental ingredient in the proposed approximation scheme of the MLE for κ. The main contribution of this paper is two fold. The first is to present a two-steps Halley based method for exploring a high-order approximation of the MLE for κ, which can significantly contribute to the improvement of estimation accuracy. The second is to develop a novel approach for the implementation of Is(x), which can make the substantial improvement of computation efficiency for computing the MLE approximation for κ. The numerical experiments were conducted to compare the proposed schemes with those in the existing works by Tanabe et al. [1] and Sra [2]. The experimental results show that, given the same amount of computation as their methods, the proposed high-order scheme can achieve much more accurate approximations while our implementation of Is(x) is preferable yet desirable for high dimensional applications.

A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of I s (x)

In high-dimensional directional statistics one of the most basic probability distributions is the von Mises-Fisher (vMF) distribution. Maximum likelihood estimation for the vMF distribution turns out to be surprisingly hard because of a difficult transcendental equation that needs to be solved for computing the concentration parameter κ. This paper is a followup to the recent paper of Tanabe et al. (Comput Stat 22(1):145–157, 2007), who exploited inequalities about Bessel function ratios to obtain an interval in which the parameter estimate for κ should lie; their observation lends theoretical validity to the heuristic approximation of Banerjee et al. (JMLR 6:1345–1382, 2005). Tanabe et al. (Comput Stat 22(1):145–157, 2007) also presented a fixed-point algorithm for computing improved approximations for κ. However, their approximations require (potentially significant) additional computation, and in this short paper we show that given the same amount of computation as their method, one can achieve more accurate approximations using a truncated Newton method. A more interesting contribution of this paper is a simple algorithm for computing I s (x): the modified Bessel function of the first kind. Surprisingly, our naive implementation turns out to be several orders of magnitude faster for large arguments common to high-dimensional data, than the standard implementations in well-established software such as Mathematica ©, Maple ©, and Gp/Pari.

Spherical Statistics and Spatial Correlation for Multielement Antenna Systems

The well-known assumption of horizontal plane wave propagation is investigated and evidence suggests that elevation plays a crucial role in defining the spatial correlation between signals on adjacent antenna array elements. To augment previously published studies, an explicit relationship between the distribution of scatterers in three-dimensional (3D) space and the spatial correlation is formulated. A novel approach is taken for modeling of the distribution of scatterers in space. More specifically, the distribution of scatterers is modeled by the 3D von Mises-Fisher (vMF) distribution. In addition, a closed-form expression is derived for the harmonic coefficients of the vMF density. The main derivation expresses the spherical harmonic coefficients associated with an arbitrary mean direction on the sphere. Further, a closed-form expression for the spatial correlation function (SCF) is derived, based on the spherical harmonic expansion (SHE) of plane waves as well as the harmonic coefficients of the expanded vMF density. A novel approach is proposed for including the effect of directional antenna responses in the SCF. Finally, the SCF is evaluated under the existence of multiple scatterer clusters in the channel.

Fast Estimation and Rendering of Indirect Highlights

AbstractThis paper proposes a method for efficiently rendering indirect highlights. Indirect highlights are caused by the primary light source reflecting off two or more glossy surfaces. Accurately simulating such highlights is important to convey the realistic appearance of materials such as chrome and shiny metal. Our method models the glossy BRDF at a surface point as a directional distribution, using a spherical von Mises‐Fisher (vMF) distribution. As our main contribution, we merge multiple vMFs into a combined multimodal distribution. This effectively creates a filtered radiance response function, allowing us to efficiently estimate indirect highlights. We demonstrate our method in a near‐interactive application for rendering scenes with highly glossy objects. Our results produce realistic reflections under both local and environment lighting.

Parameter estimation for von Mises–Fisher distributions

When analyzing high-dimensional data, it is often appropriate to pay attention only to the direction of each datum, disregarding its norm. The von Mises---Fisher (vMF) distribution is a natural pro...

Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

Several large scale data mining applications, such as text categorization and gene expression analysis, involve high-dimensional data that is also inherently directional in nature. Often such data is L2 normalized so that it lies on the surface of a unit hypersphere. Popular models such as (mixtures of) multi-variate Gaussians are inadequate for characterizing such data. This paper proposes a generative mixture-model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for data distributed on the unit hypersphere. In particular, we derive and analyze two variants of the Expectation Maximization (EM) framework for estimating the mean and concentration parameters of this mixture. Numerical estimation of the concentration parameters is non-trivial in high dimensions since it involves functional inversion of ratios of Bessel functions. We also formulate two clustering algorithms corresponding to the variants of EM that we derive. Our approach provides a theoretical basis for the use of cosine similarity that has been widely employed by the information retrieval community, and obtains the spherical kmeans algorithm (kmeans with cosine similarity) as a special case of both variants. Empirical results on clustering of high-dimensional text and gene-expression data based on a mixture of vMF distributions show that the ability to estimate the concentration parameter for each vMF component, which is not present in existing approaches, yields superior results, especially for difficult clustering tasks in high-dimensional spaces.

Generative model-based document clustering: a comparative study

This paper presents a detailed empirical study of 12 generative approaches to text clustering, obtained by applying four types of document-to-cluster assignment strategies (hard, stochastic, soft and deterministic annealing (DA) based assignments) to each of three base models, namely mixtures of multivariate Bernoulli, multinomial, and von Mises-Fisher (vMF) distributions. A large variety of text collections, both with and without feature selection, are used for the study, which yields several insights, including (a) showing situations wherein the vMF-centric approaches, which are based on directional statistics, fare better than multinomial model-based methods, and (b) quantifying the trade-off between increased performance of the soft and DA assignments and their increased computational demands. We also compare all the model-based algorithms with two state-of-the-art discriminative approaches to document clustering based, respectively, on graph partitioning (CLUTO) and a spectral coclustering method. Overall, DA and CLUTO perform the best but are also the most computationally expensive. The vMF models provide good performance at low cost while the spectral coclustering algorithm fares worse than vMF-based methods for a majority of the datasets.