
Ben Spencer, BSc, PhD (Wales) Research Interests: Global illumination. Classical ray tracing and rendering. Image processing. Visualisation. Supervisor: Dr. Mark W. Jones Address: Links: 

Publications  
Photon Parameterisation for Robust Relaxation Constraints This paper presents a novel approach to detecting and preserving fine illumination structure within photon maps. Data derived from each photon’s primal trajectory is encoded and used to build a highdimensional kdtree. Incorporation of these new parameters allows for precise differentiation between intersecting ray envelopes, thus minimizing detail degradation when combined with photon relaxation. We demonstrate how parameteraware querying is beneficial in both detecting and removing noise. We also propose a more robust structure descriptor based on principal components analysis that better identifies anisotropic detail at the subkernel level.We illustrate the effectiveness of our approach in several example scenes and show significant improvements when rendering complex caustics compared to previous methods. 

Progressive Photon Relaxation We introduce a novel algorithm for progressively removing noise from viewindependent photon maps while simultaneously minimizing residual bias. Our method refines a primal set of photons using data from multiple successive passes to estimate the incident flux local to each photon. We show how this information can be used to guide a relaxation step with the goal of enforcing a constant, perphoton flux. Using a reformulation of the radiance estimate, we demonstrate how the resulting blue noise photon distribution yields a radiance reconstruction in which error is significantly reduced. Our approach has an openended runtime of the same order as unbiased and asymptotically consistent rendering methods, converging over time to a stable result. We demonstrate its effectiveness at storing caustic illumination within a viewindependent framework and at a fidelity visually comparable to reference images rendered using progressive photon mapping. 

State of the Art in Photon Density Estimation Photondensity estimation techniques are a popular choice for simulating light transport in scenes with complicated geometry and materials. This class of algorithms can be used to accurately simulate interreflections, caustics, color bleeding, scattering in participating media, and subsurface scattering. Since its introduction, photondensity estimation has been significantly extended in computer graphics with the introduction of: specialized techniques that intelligently modify the positions or bandwidths to reduce visual error using a small number of photons, approaches that eliminate error completely in the limit, and methods that use higherorder samples and queries to reduce error in participating media. This twopart course explains how to implement all these latest advances in photondensity estimation. It begins with a short introduction using classical photon mapping, but the remainder of the course provides new, handson explanations of the latest developments in this area by experts in each technique. Attendees gain concrete and practical understanding of the latest developments in photondensityestimation techniques that have not been presented before in SIGGRAPH courses. 


Into the Blue: Better Caustics Through Photon Relaxation The photon mapping method is one of the most popular algorithms employed in computer graphics today. However, obtaining good results is dependent on several variables including kernel shape and bandwidth, as well as the properties of the initial photon distribution. While the photon density estimation problem has been the target of extensive research, most algorithms focus on new methods of optimising the kernel to minimise noise and bias. In this paper we break from convention and propose a new approach that directly redistributes the underlying photons. We show that by relaxing the initial distribution into one with a blue noise spectral signature we can dramatically reduce background noise, particularly in areas of uniform illumination. In addition, we propose an efficient heuristic to detect and preserve features and discontinuities. We then go on to demonstrate how reconfiguration also permits the use of very low bandwidth kernels, greatly improving render times whilst reducing bias.


EvenlySpaced Streamlines for Surfaces: An ImageBased Approach We introduce a novel, automatic streamline seeding algorithm for vector fields defined on surfaces in 3D space. The algorithm generates evenlyspaced streamlines fast, simply, and efficiently for any general surfacebased vector field. It is general because it handles large, complex, unstructured, adaptive resolution grids with holes and discontinuities, does not require a parameterisation, and can generate both sparse and dense representations of the flow. It is efficient because streamlines are only integrated for visible portions of the surface. It is simple because the imagebased approach removes the need to perform streamline tracing on a triangular mesh, a process which is complicated at best. And it is fast because it makes effective, balanced use of both the CPU and the GPU. The key to the algorithm’s speed, simplicity, and efficiency is its imagebased seeding strategy. We demonstrate our algorithm on complex, realworld simulation data sets from computational fluid dynamics and compare it with a objectspace streamline visualisations. • PDF (Preprint. 15.2MB) 


Hierarchical Photon Mapping Photon mapping is an efficient method for producing highquality, photorealistic images with full global illumination. In this paper we present a more accurate and efficient approach to final gathering using the photon map based upon hierarchical evaluation of the photons over each surface. We use the footprint of each gather ray to calculate the irradiance estimate area rather than deriving it from the local photon density. We then describe an efficient method for computing the irradiance from the photon map given an arbitrary estimate area. Finally, we demonstrate how the technique may be used to reduce variance and increase efficiency when sampling diffuse and glossyspecular BRDFs. 
