appleseedhq

Mentors: Student: Gray Olson

<p>Light path recording is a unique and potentially extremely valuable tool in appleseed; however, it is currently relatively limited in capability of both data capture and visualization. This project aims to help remedy both of these by improving the number of quantities that are able to be recorded, improve the visualization capabilities for these quantities, and provide a method for basic data analysis, filtering, and report generation.</p> <p>In order to do this, a main piece of the project will be to provide a unified viewport in appleseed.studio which is capable of displaying and switching between several possible views of a scene and overlay data and widgets on top of it. This will greatly improve the usability of appleseed.studio as a scene mastering tool by allowing a technical artist to view exactly how different parts of a render are being affected by lights, objects, etc. and will pave the way for even more useful data integration to be added in the future.</p>

Mentors: Student: Stephen Agyemang

<p>For my Google Summer of Code project proposal I suggest implementing the technique described in the paper “Practical Path Guiding for Efficient Light-Transport Simulation” by Thomas Müller, Markus Gross and Jan Novák. The authors describe an iterative process to learn and approximate a scene’s spatial and directional radiance distribution in a tree structure they call SD-Tree. The learned approximation is utilized for path guiding, i.e. the importance sampling of incident radiance for intelligent path construction.</p>

Mentors: Student: Kevin Masson

<p>Current implementation of adaptive sampling needs to be overwriten so that it is more efficient, easier to use for any user and more robust regarding animations. Up to now, appleseed's image plane adaptive sampler is based on a per-pixel variance analysis. To work correctly, it requires a large amount of initial samples, which is not convinient. Moreover, each pixel analysis isn't aware of its neighbours and this lead to an image still noisy.</p>

Mentors: Student: Fedor Matantsev

<p>Embree is a collection of high-performance ray tracing kernels, developed at Intel and being adopted by a range of commercial renderers such as VRay or Corona. Using embree inside appleseed might lead to significant performance gain. Moreover, there are interesting side effects: support for variety of geometry types, curve types, advanced opensubdiv caching and state-of-the-art multi-segment motion blur handling.</p> <p>I will be happy to work with appleseed and make it even better.</p> <p>I want to thank François Beaune and Esteban Tovagliari for invaluable assistance!</p>

Mentors: Student: Girish Ramesh

<p>The project aims to add three main components to the appleseed render to improve the hair and fur pipeline. Firstly, the addition of per vertex attributes to the curves primitive in order to create a more robust hair fibre curve representation. Secondly, the creation of a hair binary file format to import hair geometry from popular modelling softwares and export the format for ease of data flow in production. It will also support conversion from different binary hair file formats to the new native format. Finally, the addition of an advanced hair shading model that is tuned for production rendering. It is a physically based hair shader that captures the effects of the light scattering through hair fibres. The model is importance sampled and also has various parameters to change physical properties in the hair like refractive index and colour absorption and distribution. This project will fix one of the main deficiencies of the appleseed renderer and help the renderer challenge other production renderers for the mantle of the best.</p>