Sample Based Visibility for Soft Shadows using Alias‐free Shadow Maps

Sintorn, Erik; Eisemann, Elmar; Assarsson, Ulf

doi:10.1111/j.1467-8659.2008.01267.x

Cited by 42 publications

(63 citation statements)

References 25 publications

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“…A few recent works have explored GPU acceleration of occlusion queries [Eisemann and Decoret 2007;Forest et al 2008] and sampled visibility [Sintorn and Assarsson 2008]. It should be possible in future to combine our image-space filtering approach with these GPU methods, but we currently only use the basic Optix raytracer.…”

Section: Monte Carlo and Ray-traced Shadowsmentioning

confidence: 99%

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Axis-aligned filtering for interactive sampled soft shadows

2012

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We develop a simple and efficient method for soft shadows from planar area light sources, based on explicit occlusion calculation by raytracing, followed by adaptive image-space filtering. Since the method is based on Monte Carlo sampling, it is accurate. Since the filtering is in image-space, it adds minimal overhead and can be performed at real-time frame rates. We obtain interactive speeds, using the Optix GPU raytracing framework. Our technical approach derives from recent work on frequency analysis and sheared pixellight filtering for offline soft shadows. While sample counts can be reduced dramatically, the sheared filtering step is slow, adding minutes of overhead. We develop the theoretical analysis to instead consider axis-aligned filtering, deriving the sampling rates and filter sizes. We also show how the filter size can be reduced as the number of samples increases, ensuring a consistent result that converges to ground truth as in standard Monte Carlo rendering.

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Section: Monte Carlo and Ray-traced Shadowsmentioning

confidence: 99%

“…Moreover, we are an (many) order of magnitude faster than most prior work on offline soft shadows, since we build on a GPU-accelerated raytracer. In the future, further speedups could potentially be achieved by also GPU-accelerating the visibility sampling [Sintorn and Assarsson 2008].…”

Section: Accuracymentioning

confidence: 99%

Axis-aligned filtering for interactive sampled soft shadows

2012

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“…Here, the view samples are projected into light space and are used as the sample positions in a subsequent shadow map rendering pass [Aila and Laine 2004;Johnson et al 2005]. The same idea has been applied to soft shadows, where the shadow casting geometry is enlarged to cover all potentially occluded samples [Sintorn et al 2008;Johnson et al 2009]. This approach can lower the fill-rate demands, compared to shadow-volume based methods, but rendering speeds are still highly dependent on the amount of shadow casting geometry that needs to be processed.…”

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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“…This approach has been attempted several times before (e.g. [Aila and Laine 2004;Sintorn et al 2008;Sintorn et al 2011]) but in this paper we will suggest that the quality of the acceleration structure is critical to obtaining good and reliable performance, and so we will generate a tightly fitting, fully three-dimensional hierarchy.…”

Section: Algorithmmentioning

confidence: 99%

“…While GPU-based implementations of these algorithms exist (e.g. [Sintorn et al 2008]), the irregular rasterization process often leads to very unbalanced workloads, which results in uneven and potentially very poor performance.…”

Section: Introduction and Previous Workmentioning

confidence: 99%

Per-triangle shadow volumes using a view-sample cluster hierarchy

Sintorn

Kämpe

Olsson

et al. 2014

Proceedings of the 18th Meeting of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games

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Rendering pixel-accurate shadows in scenes lit by a point lightsource in real time is still a challenging problem. For scenes of moderate complexity, algorithms based on Shadow Volumes are by far the most efficient in most cases, but traditionally, these algorithms struggle with views where the volumes generate a very high depth complexity. Recently, a method was suggested that alleviates this problem by testing each individual triangle shadow volume against a hierarchical depth map, allowing volumes that are in front of, or behind, the rendered view samples to be efficiently culled. In this paper, we show that this algorithm can be greatly improved by building a full 3D acceleration structure over the view samples and testing per-triangle shadow volumes against that. We show that our algorithm can elegantly maintain high frame-rates even for views with very high-frequency depth-buffers where previous algorithms perform poorly. Our algorithm also performs better than previous work in general, making it, to the best of our knowledge, the fastest pixel-accurate shadow algorithm to date. It can be used with any arbitrary polygon soup as input, with no restrictions on geometry or required pre-processing, and trivially supports transparent and textured shadow-casters.

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Sample Based Visibility for Soft Shadows using Alias‐free Shadow Maps

Cited by 42 publications

References 25 publications

Axis-aligned filtering for interactive sampled soft shadows

Axis-aligned filtering for interactive sampled soft shadows

High resolution sparse voxel DAGs

Per-triangle shadow volumes using a view-sample cluster hierarchy

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