Efficient Post-processing with Importance Sampling

نویسندگان

  • Balázs Tóth
  • László Szirmay-Kalos
  • Tamás Umenhoffer
چکیده

Introduction Texture filtering is a critical part in many rendering and post-processing methods. If we do it naively, the fragment shader needs to access the texture memory many times to fetch values in the neighborhood of the processed texel. This article presents an efficient filtering algorithm that minimizes the number of texture fetches. The algorithm is based on importance sampling and also exploits the bi-linear filtering hardware. We also present applications in one, two, and even in three dimensions, such as tone mapping with glow, depth of field, and real-time local ambient occlusion.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Edge detection using sub-sampled k-space data: application to upper airway MRI

Introduction: The detection of tissue borders is of great importance in several MRI applications. Edge detection is typically performed as a post-processing step, using magnitude images that are reconstructed from fully-sampled k-space data. In dynamic imaging (e.g. of human speech, ventricular function, and joint kinematics), tissue borders often comprise the primary information of interest. I...

متن کامل

Combined Correlated and Importance Sampling in Direct Light Source Computation and Environment Mapping

This paper presents a general variance reduction method that is a quasi-optimal combination of correlated and importance sampling. The weights of the combination are selected automatically in order to keep the merits of both importance and correlated sampling. The proposed sampling method is used for efficient direct light source computation of large area sources and for the calculation of the ...

متن کامل

Efficient Calculation of Risk Measures by Importance Sampling – the Heavy Tailed Case

Computation of extreme quantiles and tail-based risk measures using standard Monte Carlo simulation can be inefficient. A method to speed up computations is provided by importance sampling. We show that importance sampling algorithms, designed for efficient tail probability estimation, can significantly improve Monte Carlo estimators of tail-based risk measures. In the heavy-tailed setting, whe...

متن کامل

Efficient Heuristics for Simulating Population Overflow in Feed-forward Networks

In this paper we propose a state-dependent importance sampling heuristic to estimate the probability of population overflow in feed-forward networks. This heuristic attempts to approximate the “optimal” state-dependent change of measure without the need for difficult analysis or costly optimization involved in other recently proposed adaptive importance sampling algorithms. Preliminary simulati...

متن کامل

Sample Efficient Actor-Critic with Experience Replay

This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems. To achieve this, the paper introduces several innovations, including truncated importance sampling with bias correction, stocha...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014