Axis-aligned filtering for interactive sampled soft shadows

Mehta, S.A.; Wang, Brandon; Ramamoorthi, Ravi

doi:10.1145/2366145.2366182

Cited by 43 publications

(70 citation statements)

References 44 publications

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“…Mehta et al [MWR12] observed that the 4D sheared filtering approach developed in previous methods [ETH * 09, EHDR11,EDR11] adds minutes of overhead to the rendering time. In addition, the two step algorithm requires additional storage for the initial samples.…”

Section: Local Frequency Analysismentioning

confidence: 99%

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Zwicker

Jarosz

Lehtinen

et al. 2015

Computer Graphics Forum

125

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Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between "a priori" methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and "a posteriori" methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.

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Section: Local Frequency Analysismentioning

confidence: 99%

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Zwicker

Jarosz

Lehtinen

et al. 2015

Computer Graphics Forum

125

View full text Add to dashboard Cite

show abstract

“…Based on the same frequency analysis reasoning, a much faster reconstruction algorithm is presented by Mehta et al [2012]. These algorithms are orthogonal to the work presented in our paper and could potentially be used to extend our approach.…”

Section: Previous Workmentioning

confidence: 99%

High resolution sparse voxel DAGs

2013

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Figure 1: The EPICCITADEL scene voxelized to a 128K 3 (131 072 3 ) resolution and stored as a Sparse Voxel DAG. Total voxel count is 19 billion, which requires 945MB of GPU memory. A sparse voxel octree would require 5.1GB without counting pointers. Primary shading is from triangle rasterization, while ambient occlusion and shadows are raytraced in the sparse voxel DAG at 170 MRays/sec and 240 MRays/sec respectively, on an NVIDIA GTX680. AbstractWe show that a binary voxel grid can be represented orders of magnitude more efficiently than using a sparse voxel octree (SVO) by generalising the tree to a directed acyclic graph (DAG). While the SVO allows for efficient encoding of empty regions of space, the DAG additionally allows for efficient encoding of identical regions of space, as nodes are allowed to share pointers to identical subtrees. We present an efficient bottom-up algorithm that reduces an SVO to a minimal DAG, which can be applied even in cases where the complete SVO would not fit in memory. In all tested scenes, even the highly irregular ones, the number of nodes is reduced by one to three orders of magnitude. While the DAG requires more pointers per node, the memory cost for these is quickly amortized and the memory consumption of the DAG is considerably smaller, even when compared to an ideal SVO without pointers. Meanwhile, our sparse voxel DAG requires no decompression and can be traversed very efficiently. We demonstrate this by ray tracing hard and soft shadows, ambient occlusion, and primary rays in extremely high resolution DAGs at speeds that are on par with, or even faster than, state-of-the-art voxel and triangle GPU ray tracing.

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“…To realize soft shadows by a ray tracing approach, in interactive frame rate, a screen space filtering has also been introduced by Mehta et al [22]. This method also includes a separable filtering but our implementation for the filtering more carefully takes into account the case of complex scenes.…”

Section: Related Workmentioning

confidence: 99%

Efficient Screen Space Anisotropic Blurred Soft Shadows

Zheng

Saito

2014

IEICE Trans. Inf. & Syst.

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SUMMARYShadow mapping is an efficient method to generate shadows in real time computer graphics and has broad variations from hard to soft shadow synthesis. Soft shadowing based on shadow mapping is a blurring technique on a shadow map or on screen space. Blurring on screen space has an advantage for efficient sampling on a shadow map, since the blurred target array has exactly the same coordinates as the screen. However, a previous blurring method on screen space has a drawback: the generated shadow is not correct when a view direction has a large angle to the normal of the shadowed plane. In this paper, we introduce a new screen space based method for soft shadowing that is fast and generates soft shadows more accurately than the previous screen space soft shadow mapping method. The resultant images show shadows produced by our method just stand in the same place, while shadows by the previous method change in terms of penumbra while the view moves. Surprisingly, although our method is more complex than the previous method, the measurement results of the calculation time show our method is almost the same performance. This is because it controls the blurring area more accurately and thus successfully reduces multiplications for blurring.

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Axis-aligned filtering for interactive sampled soft shadows

Cited by 43 publications

References 44 publications

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering

High resolution sparse voxel DAGs

Efficient Screen Space Anisotropic Blurred Soft Shadows

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