- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Geometry-aware metropolis light transport
摘要： Markov chain Monte Carlo (MCMC) rendering utilizes a sequence of correlated path samples which is obtained by iteratively mutating the current state to the next. The efficiency of MCMC rendering depends on how well the mutation strategy is designed to adapt to the local structure of the state space. We present a novel MCMC rendering method that automatically adapts the step sizes of the mutations to the geometry of the rendered scene. Our geometry-aware path space perturbation largely avoids tentative samples with zero contribution due to occlusion. Our method limits the mutation step size by estimating the maximum opening angle of a cone, centered around a segment of a light transport path, where no geometry obstructs visibility. This geometry-aware mutation increases the acceptance rates, while not degrading the sampling quality. As this cone estimation introduces a considerable overhead if done naively, to make our approach efficient, we discuss and analyze fast approximate methods for cone angle estimation which utilize the acceleration structure already present for the ray-geometry intersection. Our new approach, integrated into the framework of Metropolis light transport, can achieve results with lower error and less artifact in equal time compared to current path space mutation techniques.
关键词： global illumination,Markov chain Monte Carlo light transport
更新于2025-09-23 15:23:52
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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Rome (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - Bayesian Restoration of High-Dimensional Photon-Starved Images
摘要： This paper investigates different algorithms to perform image restoration from single-photon measurements corrupted with Poisson noise. The restoration problem is formulated in a Bayesian framework and several state-of-the-art Monte Carlo samplers are considered to estimate the unknown image and quantify its uncertainty. The different samplers are compared through a series of experiments conducted with synthetic images. The results demonstrate the scaling properties of the proposed samplers as the dimensionality of the problem increases and the number of photons decreases. Moreover, our experiments show that for a certain photon budget (i.e., acquisition time of the imaging device), downsampling the observations can yield better reconstruction results.
关键词： Bayesian statistics,Markov chain Monte Carlo,Inverse problems,Image processing,Bouncy particle sampler,Poisson noise
更新于2025-09-23 15:22:29
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Sampling Technique for Defining Segmentation Error Margins with Application to Structural Brain Mri
摘要： Image segmentation is often considered a deterministic process with a single ground truth. Nevertheless, in practice, and in particular, when medical imaging analysis is considered, the extraction of regions of interest (ROIs) is ill-posed and the concept of 'most probable' segmentation is model-dependent. In this paper, a measure for segmentation uncertainty in the form of segmentation error margins is introduced. This measure provides a goodness quantity and allows a 'fully informed' comparison between extracted boundaries of related ROIs as well as more meaningful statistical analysis. The tool we present is based on a novel technique for segmentation sampling in the Fourier domain and Markov Chain Monte Carlo (MCMC). The method was applied to cortical and sub-cortical structure segmentation in MRI. Since the accuracy of segmentation error margins cannot be validated, we use receiver operating characteristic (ROC) curves to support the proposed method. Precision and recall scores with respect to expert annotations suggest this method as a promising tool for a variety of medical imaging applications including user-interactive segmentation, patient follow-up, and cross-sectional analysis.
关键词： Fourier domain,Segmentation uncertainty margins,sampling,MRI,Markov Chain Monte Carlo
更新于2025-09-23 15:21:01
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[IEEE 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) - St. Petersburg and Moscow, Russia (2020.1.27-2020.1.30)] 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) - The Study of Three-dimensional Tissue-engineering Structures Obtained Using Laser Printing for Muscle Regeneration
摘要： Existing optimization methods to heterogeneous redundancy allocation problem often suffer from the local-trap problem in optimization, due to the rugged energy landscapes. In this paper, a new optimization paradigm based on the Markov chain Monte Carlo sampling is proposed for solving the heterogeneous redundancy allocation for multi-state systems. We address this in an optimization-by-sampling framework, and propose to sample the intricate distribution over the combinatorial space by a doubly adaptive sampling approach, where the target adaptation favors free random walk on the rugged energy landscape to substantially alleviate the local-trap problem by updating the target distribution on-the-fly, while the proposal adaptation helps improve the sampling efficiency by learning the proposal distribution based on chain history in optimization. Experimental results performed on a range of benchmark instances demonstrated the superiority of the proposed optimization approach compared with the state-of-the-art alternatives in terms of the solution quality or computational efficiency.
关键词： multi-state system,Optimization-by-sampling,Markov chain Monte Carlo,redundancy allocation problem
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - High-efficient Cd-free CZTS solar cells achieved by nanoscale atomic layer deposited aluminium oxide
摘要： Existing optimization methods to heterogeneous redundancy allocation problem often suffer from the local-trap problem in optimization, due to the rugged energy landscapes. In this paper, a new optimization paradigm based on the Markov chain Monte Carlo sampling is proposed for solving the heterogeneous redundancy allocation for multi-state systems. We address this in an optimization-by-sampling framework, and propose to sample the intricate distribution over the combinatorial space by a doubly adaptive sampling approach, where the target adaptation favors free random walk on the rugged energy landscape to substantially alleviate the local-trap problem by updating the target distribution on-the-fly, while the proposal adaptation helps improve the sampling efficiency by learning the proposal distribution based on chain history in optimization. Experimental results performed on a range of benchmark instances demonstrated the superiority of the proposed optimization approach compared with the state-of-the-art alternatives in terms of the solution quality or computational efficiency.
关键词： Optimization-by-sampling,Markov chain Monte Carlo,multi-state system,redundancy allocation problem
更新于2025-09-19 17:13:59
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[IEEE 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Xiamen, China (2019.12.17-2019.12.20)] 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall) - Research of PAM-4 Modulated WDM-PON Architecture for 5G Millimeter-wave Hybrid Photonics-wireless Interface
摘要： Existing optimization methods to heterogeneous redundancy allocation problem often suffer from the local-trap problem in optimization, due to the rugged energy landscapes. In this paper, a new optimization paradigm based on the Markov chain Monte Carlo sampling is proposed for solving the heterogeneous redundancy allocation for multi-state systems. We address this in an optimization-by-sampling framework, and propose to sample the intricate distribution over the combinatorial space by a doubly adaptive sampling approach, where the target adaptation favors free random walk on the rugged energy landscape to substantially alleviate the local-trap problem by updating the target distribution on-the-fly, while the proposal adaptation helps improve the sampling efficiency by learning the proposal distribution based on chain history in optimization. Experimental results performed on a range of benchmark instances demonstrated the superiority of the proposed optimization approach compared with the state-of-the-art alternatives in terms of the solution quality or computational efficiency.
关键词： Markov chain Monte Carlo,multi-state system,Optimization-by-sampling,redundancy allocation problem
更新于2025-09-19 17:13:59
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Bayesian 3D Reconstruction of Subsampled Multispectral Single-photon Lidar Signals
摘要： Light detection and ranging (Lidar) single-photon devices capture range and intensity information from a 3D scene. This modality enables long range 3D reconstruction with high range precision and low laser power. A multispectral single-photon Lidar system provides additional spectral diversity, allowing the discrimination of different materials. However, the main drawback of such systems can be the long acquisition time needed to collect enough photons in each spectral band. In this work, we tackle this problem in two ways: first, we propose a Bayesian 3D reconstruction algorithm that is able to find multiple surfaces per pixel, using few photons, i.e., shorter acquisitions. In contrast to previous algorithms, the novel method processes jointly all the spectral bands, obtaining better reconstructions using less photon detections. The proposed model promotes spatial correlation between neighbouring points within a given surface using spatial point processes. Secondly, we account for different spatial and spectral subsampling schemes, which reduce the total number of measurements, without significant degradation of the reconstruction performance. In this way, the total acquisition time, memory requirements and computational time can be significantly reduced. The experiments performed using both synthetic and real single-photon Lidar data demonstrate the advantages of tailored sampling schemes over random alternatives. Furthermore, the proposed algorithm yields better estimates than other existing methods for multi-surface reconstruction using multispectral Lidar data.
关键词： Poisson noise,Bayesian inference,multispectral imaging,Lidar,3D reconstruction,Markov chain Monte Carlo
更新于2025-09-11 14:15:04
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Survey of Markov Chain Monte Carlo Methods in Light Transport Simulation
摘要： Two decades have passed since the introduction of Markov chain Monte Carlo (MCMC) into light transport simulation by Veach and Guibas, and numerous follow-up works have been published since then. However, up until now no survey has attempted to cover the majority of these methods. The aim of this paper is therefore to offer a first comprehensive survey of MCMC algorithms for light transport simulation. The methods presented in this paper are categorized by their objectives and properties, while we point out their strengths and weaknesses. We discuss how the methods handle the main issues of MCMC and how they could be combined or improved in the near future. To make the paper suitable for readers unacquainted with MCMC methods, we include an introduction to general MCMC and its demonstration on a simple example.
关键词： Light Transport Simulation,Markov Chain Monte Carlo,Metropolis-Hastings,STAR,Metropolis Light Transport
更新于2025-09-04 15:30:14