The Manuka rendering architecture has been designed in the spirit of the classic reyes rendering architecture: to enable the creation of visually rich computer generated imagery for visual effects in movie production. Following in the footsteps of reyes over the past 30 years, this means supporting extremely complex geometry, texturing, and shading. In the current generation of renderers, it is essential to support very accurate global illumination as a means to naturally tie together different assets in a picture. This is commonly achieved with Monte Carlo path tracing, using a paradigm often called shade on hit, in which the renderer alternates tracing rays with running shaders on the various ray hits. The shaders take the role of generating the inputs of the local material structure, which is then used by path-sampling logic to evaluate contributions and to inform what further rays to cast through the scene. We propose a shade before hit paradigm instead and minimise I/O strain on the system, leveraging locality of reference by running pattern generation shaders before we execute light transport simulation by path sampling. We describe a full architecture built around this approach, featuring spectral light transport and a flexible implementation of multiple importance sampling (mis), resulting in a system able to support a comparable amount of extensibility to what made the reyes rendering architecture successful over many decades.

Manuka: A Batch-Shading Architecture for Spectral Path Tracing in Movie Production

In the past few years, advances in graphics hardware have fuelled an explosion of research and development in the field of interactive and real‐time rendering in screen space. Following this trend, a rapidly increasing number of applications rely on multifragment rendering solutions to develop visually convincing graphics applications with dynamic content. The main advantage of these approaches is that they encompass additional rasterised geometry, by retaining more information from the fragment sampling domain, thus augmenting the visibility determination stage. With this survey, we provide an overview of and insight into the extensive, yet active research and respective literature on multifragment rendering. We formally present the multifragment rendering pipeline, clearly identifying the construction strategies, the core image operation categories and their mapping to the respective applications. We describe features and trade‐offs for each class of techniques, pointing out GPU optimisations and limitations and provide practical recommendations for choosing an appropriate method for each application. Finally, we offer fruitful context for discussion by outlining some existing problems and challenges as well as by presenting opportunities for impactful future research directions.

A Survey of Multifragment Rendering

We present a novel algorithm to denoise deep Monte Carlo renderings, in which pixels contain multiple colour values, each for a different range of depths. Deep images are a more expressive representation of the scene than conventional flat images. However, since each depth bin receives only a fraction of the flat pixel's samples, denoising the bins is harder due to the less accurate mean and variance estimates. Furthermore, deep images lack a regular structure in depth—the number of depth bins and their depth ranges vary across pixels. This prevents a straightforward application of patch‐based distance metrics frequently used to improve the robustness of existing denoising filters. We address these constraints by combining a flat image‐space non‐local means filter operating on pixel colours with a deep cross‐bilateral filter operating on auxiliary features (albedo, normal, etc.). Our approach significantly reduces noise in deep images while preserving their structure. To our best knowledge, our algorithm is the first to enable efficient deep‐compositing workflows with denoised Monte Carlo renderings. We demonstrate the performance of our filter on a range of scenes highlighting the challenges and advantages of denoising deep images.

Denoising Deep Monte Carlo Renderings

This paper analyses methodological and conceptual shifts in filmmaking brought about by the integrated use of VR technology, game engines, and LED walls in a Virtual Production Studio (VPS). The paper focuses not only on the components of an integrated VPS system, but also on what makes it qualitatively different from traditional filmmaking technologies. The VPS concept transforms the relationships among display technology, live action, image production, and postproduction (VFX) by causing a qualitative and performative shift in filmmaking. The paper analyses the current state of VP research from both conceptual and technological perspectives, global VP facilities, and significant trends in Australia by presenting two business case studies. We have observed three major trends: Globalisation, Collaboration and Industrialisation. The paper concludes by stating that there is a need for an integrated VPS network through shared infrastructure and resources to foster high quality research and to solve industry-identified problems through industry-led collaborative research partnerships. 

The Virtual Production Studio Concept – An Emerging Game Changer in Filmmaking

There are various approaches to storing numeric IDs as extra channels within CG rendered images. Using these channels, individual objects can be selected and separately modified. To associate an object with a text string a table or manifest is required mapping numeric IDs to text strings. This allows readable identification of ID-based selections, as well as the ability to make a selection using a text search. A scheme for storage of this ID Manifest is proposed which is independent of the approach used to store the IDs within the image. The total size of the raw strings within an ID Manifest may be very large but often contains much repeated information. A novel compression scheme is therefore employed which significantly reduces the size of the manifest.

A scheme for storing object ID manifests in openEXR images

We transform irregularly sampled shadow map data to deep image buffers in camera space, which are then used to create volumetric shadows in a deep compositing workflow. Our technique poses no restrictions on the sample locations of the shadow map and can thus be used with a variety of adaptive approaches to produce more precise shadows closer to the camera. To construct watertight shafts towards the light source forming crepuscular rays, we use a two-dimensional quad tree in light space. This structure is constructed from the shadow samples independent of the camera position, making stereo renders and camera animations for static light sources and geometry more efficient. The actual integration of volumetric light transport is then left to a fast image space deep compositing workflow, enabling short turnaround times for cinematic lighting design. We show a simple scalable ray tracing kernel to convert the quad tree representation to a deep image for each camera, where ray tracing takes only 25% of the processing time.

Camera space volumetric shadows

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Camera space volumetric shadows

A scheme for storing object ID manifests in openEXR images