Rasterisation-based progressive photon mapping
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ORIGINAL ARTICLE
Rasterisation-based progressive photon mapping Iordanis Evangelou1
· Georgios Papaioannou1 · Konstantinos Vardis1 · Andreas A. Vasilakis1
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Ray tracing on the GPU has been synergistically operating alongside rasterisation in interactive rendering engines for some time now, in order to accurately capture certain illumination effects. In the same spirit, in this paper, we propose an implementation of progressive photon mapping entirely on the rasterisation pipeline, which is agnostic to the specific GPU architecture, in order to synthesise images at interactive rates. While any GPU ray tracing architecture can be used for photon mapping, performing ray traversal in image space minimises acceleration data structure construction time and supports arbitrarily complex and fully dynamic geometry. Furthermore, this strategy maximises data structure reuse by encompassing rasterisation, ray tracing and photon gathering tasks in a single data structure. Both eye and light paths of arbitrary depth are traced on multi-view deep G-buffers, and photon flux is gathered by a properly adapted multi-view photon splatting. In contrast to previous methods exploiting rasterisation to some extent, due to our novel indirect photon splatting approach, any event combination present in photon mapping is captured. We evaluate our method using typical test scenes and scenarios for photon mapping methods and show how our approach outperforms typical GPU-based progressive photon mapping. Keywords Photon mapping · Rasterisation · Ray tracing
1 Introduction Photon mapping [7,8] is a well-known two-stage approximation to bidirectional path tracing, where light-carrying paths or photons deposit and cache the carried flux on non-specular surfaces, pre-multiplied with the light path throughput. A data structure, the photon map, is responsible for the storage and fast indexing of these particles. Subsequently, for multiple paths traced from the camera, the contribution of photons to hit points on non-specular interfaces is estimated, converting flux to radiance and modulating it with the combined throughput to the sensor. Since the probability of path vertices from the camera and photon tracing coinciding is zero, photon mapping relies on a kernel function that performs photon flux density estimation to integrate the contribution of particles in the vicinity of a camera hit point. As the stored particles in the photon map increase, if a kernel enclosing a fixed number of particles is used, denser areas will result in a tighter and more accurate estimator of scattered radiance, while wider ones may introduce significant bias. The
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Iordanis Evangelou [email protected]
considerable storage required by the photon map(s) and the inability to predict how many photons are adequate to converge to an accurate image estimation for a particular scene led into what is known as the progressive photon mapping algorithm [4] (PPM). Its subsequent evolution led to the probab
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