Abstract Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, visual quality rendered images often suffers from estimator variance, which appears visually distracting noise. Adaptive sampling reconstruction reduce variance by controlling density aggregating samples in a step, possibly over large regions. In this paper we survey recent advances area. We distinguish between “a...

We address the problem of denoising Monte Carlo renderings by studying existing approaches and proposing a new algorithm that yields state-of-the-art performance on wide range scenes. analyze from theoretical empirical point view, relating strengths limitations their corresponding components with an emphasis production requirements. The observations our analysis instruct design filter offers high-quality results stable performance. A key observation is using auxiliary buffers (normal,...

Perceptually lossless foveated rendering methods exploit human perception by selectively at different quality levels based on eye gaze (at a lower computational cost) while still maintaining the user's of full render. We consider three and propose practical rules thumb for each method to achieve significant performance gains in real-time frameworks. Additionally, we contribute new metric perceptual building HDR-VDP2 that, unlike traditional metrics, considers loss fidelity peripheral vision...

Monte Carlo ray tracing is considered one of the most effective techniques for rendering photo-realistic imagery, but requires a large number samples to produce converged or even visually pleasing images. We develop novel image-plane adaptive sampling and reconstruction method based on local regression theory. A space estimation process proposed employing regression, by robustly addressing noisy high-dimensional features. Given estimated space, we provide two-step optimization selecting...

In this paper, we propose a new adaptive rendering method to improve the performance of Monte Carlo ray tracing, by reducing noise contained in rendered images while preserving high-frequency edges. Our locally approximates an image with polynomial functions and optimal order each function is estimated so that our reconstruction error can be minimized. To robustly estimate order, multi-stage estimation process iteratively estimates error. addition, present energy-preserving outlier removal...

Abstract We propose an efficient and robust image‐space denoising method for noisy images generated by Monte Carlo ray tracing methods. Our is based on two new concepts: virtual flash homogeneous pixels. Inspired recent developments in photography, emulate photographs taken with a flash, to capture various features of rendered without taking additional samples. Using image as edge‐stopping function, our can preserve that were not captured well only existing functions such normals depth...

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We present a cache-oblivious ray reordering method for tracing. Many global illumination methods such as path tracing and photon mapping use generate lots of rays to simulate various realistic visual effects. However, these tend be very incoherent show lower cache utilizations during models. In order address this problem improve the coherence, we propose novel Hit Point Heuristic (HPH) compute coherent ordering rays. The HPH uses hit points between scene measure. reorder by using...

We propose a new adaptive rendering algorithm that enhances the performance of Monte Carlo ray tracing by reducing noise, i.e., variance, while preserving variety high-frequency edges in rendered images through novel prediction based reconstruction. To achieve our goal, we iteratively build multiple, but sparse linear models. Each model has its window, where predicts unknown ground truth image can be generated with an infinite number samples. Our method recursively estimates errors...

We present a novel compressed bounding volume hierarchy (BVH) representation, random-accessible hierarchies (RACBVHs), for various applications requiring random access on BVHs of massive models. Our RACBVH representation is compact and transparently supports the without decompressing whole BVH. To support our BVHs, we decompose BVH into set clusters. Each cluster contains consecutive (BV) nodes in original layout Also, each separately from other clusters serves as an point to representation....

Monte Carlo integration is an efficient method to solve a high-dimensional integral in light transport simulation, but it typically produces noisy images due its stochastic nature. Many existing methods, such as image denoising and gradient-domain reconstruction, aim mitigate this noise by introducing some form of correlation among pixels. While those methods reduce noise, they are known still suffer from method-specific residual or systematic errors. We propose unified framework that...

Unbiased rendering algorithms such as path tracing produce accurate images given a huge number of samples, but in practice, the techniques often leave visually distracting artifacts (i.e., noise) their rendered due to limited time budget. A favored approach for mitigating noise problem is applying learning-based denoisers unbiased noisy and suppressing while preserving image details. However, denoising typically introduce systematic error, i.e., bias, which does not decline rapidly when...

Using a network trained by large dataset is becoming popular for denoising Monte Carlo rendering. Such approach based on supervised learning currently considered the best in terms of quality. Nevertheless, this may fail when image to be rendered (i.e., test data) has very different characteristics than images included training dataset. A pre-trained not properly denoise such an since it unseen data from perspective. To address fundamental issue, we introduce post-processing that improves...

Direct volume rendering (DVR) using volumetric path tracing (VPT) is a scientific visualization technique that simulates light transport with objects' matter physically-based lighting models. Monte Carlo (MC) often used surface models, yet its application for models difficult due to the complexity of integrating MC light-paths in media none or smooth material boundaries. Moreover, auxiliary geometry-buffers (G-buffers) produced volumes are typically very noisy, failing guide image denoisers...

Compelling virtual reality experiences require high quality imagery as well head motion with six degrees of freedom. Most existing systems limit the viewer (prerecorded fixed position 360 video panoramas), or are limited in realism, e.g. game graphics rendered real-time on low powered devices. We propose a solution for presenting movie to user while still allowing sense presence afforded by free viewpoint motion. By transforming offline content into novel immersive deep media representation,...

Abstract In this paper, we propose a new technique to incorporate recent adaptive rendering approaches built upon local regression theory into gradient‐domain path tracing framework, in order achieve high‐quality results. Our method aims reduce random artifacts introduced by sampling on image colors and gradients. high‐level approach is identify feature from noisy gradients, pass the an existing based so that reconstruction using our can boost performance of rendering. To fulfill idea,...

Images taken in low light conditions typically contain distracting noise, and eliminating such noise is a crucial computer vision problem. Additional photos captured with camera flash can guide an image denoiser to preserve edges since the images often fine details reduced noise. Nonetheless, be misled by inconsistent images, which have structures (e.g., edges) that do not exist no-flash images. Unfortunately, this disparity frequently occurs as flash/no-flash pairs are different conditions....

Abstract Hierarchical culling is a key acceleration technique used to efficiently handle massive models for ray tracing, collision detection, etc. To support such hierarchical culling, bounding volume hierarchies (BVHs) combined with meshes are widely used. However, BVHs may require very large amount of memory space, which can negate the benefits using BVHs. address this problem, we present novel hierarchical‐culling oriented compact mesh representation, HCCMesh , tightly integrates and BVH...

Abstract We propose a new real‐time temporal filtering and antialiasing (AA) method for rasterization graphics pipelines. Our is based on Pixel History Linear Models (PHLM), concept modeling the history of pixel shading values over time using linear models. Based PHLM, our can predict per‐pixel variations function between consecutive frames. This combines reprojection with predictions in order to provide temporally coherent shading, even presence very noisy input images. address both spatial...

Image-space denoising techniques have been widely employed in Monte Carlo rendering, typically blending neighboring pixel estimates using a kernel. It is recognized that kernel should be adapted to characteristics of the input order ensure robustness diverse image features and amount noise. Denoising with such an input-dependent kernel, however, can introduce bias makes denoised estimate even less accurate than noisy estimate. Consequently, it has considered essential balance introduced by...

In this paper, we present a new approach, AKIMap, that uses an adaptive kernel inference for dense and sharp occupancy grid representations. Our approach is based on the multivariate estimation, propose simple, two-stage method selects bandwidth matrix efficient accurate estimation. To utilize correlations of observations given sparse non-uniform distributions point samples, to use covariance as initial matrix, then optimize by adjusting its scale in efficient, data-driven way on-the-fly...

In this paper we propose Pixel-based Random Parameter Filtering (P-RPF) for efficiently denoising images generated from complex illuminations with a high sample count. We design various operations of our method to have time complexity that is independent the number samples per pixel. compute feature weights by measuring functional relationships between MC inputs and output in basis. To accelerate sample-basis process use an up sampling weights. applied wide variety models different rendering...