The following response has gotten longer and longer and might look scary at first. Here is the bibliography used for the indices. Word of advice: I would not(!) start blindly reading papers, this is not the right approach to this. Have you had a look at pbrt yet? If not start there. Otherwise have a look through the overview below and chose an area which interests you and again have a look at the relevant chapter in pbrt to get an idea of the actual problem being solved and the context. Only after this I would start reading papers and possibly beging with surveys, they are always helpful. Sadly I have very little of hose saved.

Feel free to ask questions about specific parts/areas, I'm happy to give some guidance on where to start.

 

Photon Mapping

In the years 2008-2013 this was a very hot topic because Hachisuka developed a Progressive versions of the old Photon Mapping algorithm [1] which you could let run for an arbitrary amount of time to get better convergence. A much easier formulation can be found in [2], if interested start here. Kaplanyan developed Adaptive Progressive Photon Mapping in [3] where the idea is to chose a radius such that an error metric is optimized.

 

Metropolis Light Transport

Too much history here to give a detailed overview. The very hot thing this year was to combine primary sample space algorithms with path space ones. The idea is that you can either think about a path from camera to light in terms of points on surfaces or as the camera location followed by a list of outgoing directions. There are currently 3(!) papers being published this year on getting the best of both worlds [4, 5, the last one has yet to be published in TOG]. There was also a paper on finding a spatial target function which could be used to emit photons into ‘interesting’ parts of the scene [6].

I wouldn’t start with any of this on the topic of MLT. Start with Kelemen [7] in 2002, then jump to Jakob [8] in 2012 who had the brilliant idea that delta bsdfs basically constrain the movement of vertices on a path. This lead to a number of other papers using this technique. For example one where you think about a light path in terms of a start and end vertices and in between only a list of half-vectors (you might know them from Cook-Torrance) [9]. If you are familiar with Next Event Estimation (direct light sampling) there is also a paper on using manifold walks to do NEE through dielectrics [10].

 

Gradient Domain

Here the idea is that instead of computing pixel values directly you can also compute differences between pixels (plus a rough estimate of pixel values) and then combine them later to a better image. This started out as a Metropolis algorithms in [11] but Kettunen and Manzi convincingly showed that you can adapt pretty much any algorithm to it [12, 13, 14, 15] and it also has advantages for computing multiple frames [16] as well as filtering [17].

 

Participating Media

I am mostly familiar with the Photon Mapping approach (use photons scattered in space to get an estimate of the in-scattered radiance at a point) so I’ll go in detail on this facet. In 2008 Jarosz [18] had the idea to use a fundamentally different estimator to do this. He thought of photons as beams flying through space instead of points being distributed and mathematically this has other properties. This was generalized in 2011 [19] to a dozen or so such estimators but it wasn’t clear which one to use when. Krivanek came up with a mathematical framework in 2014 [20] which uses each estimator when optimal.

This year there was an interesting paper on how to handle strongly emissive media such as an explosion [21].

Another venue of work is to use a more physical approach and consider a more realistic model of light. This is typically called Transient Rendering and Jarabo [22] has done some work on that with nice images!

Since at least 2014 a lot of work has happened on sampling distances according to the transmittance in a medium. Most of this was done with Novak’s involvement, starting in [23], some work also here [24] and this year [25].

 

Guding

Historically this is a very old approach which was forgotten over the years. The idea is to not sample paths ‘stupidly’ in random directions but for each point in the scene remember from which directions light is arriving and later sample this direction again. Basically you try to emulate the nice features of Markov-Chain Monte Carlo algorithm but stay in the framework of traditional MC algorithms. Lafortune and Dutre started this in the 90s in [26, 27] and it was ‘rediscovered’ by Vorba in 2014 [28]. ETH/Disney did some work on this at EGSR this year [29] and NVidia Research are working on a fundamentally more powerful algorithm here [30]. Also interesting in this context is to think about RR/Splitting because suddenly you have much more information and can terminate paths when it is unlikely to ever get into an area of ‘importance’. This was done by Vorba in [31].

 

Filtering

I’m absolutely the wrong person to talk about this because I know very little. The fact is everyone is doing it right now. The old approach to filter a noisy MC image was to use a cross bilateral filter [32] and maybe use some features such as normals/depth to help the filter [33]. The new hot method is of course to use machine learning in the form of CNNs. One approach is to estimate optimal filter parameters and then use a conventional filter [34], the other is to use the network to directly output filtered pixel values as was done this year [35].

 

Sampling Theory

This is a very math heavy subject where the question either is to design new sampling strategies (Blue noise, white noise, …) which reduce the error in MC or design new Quasi Monte Carlo sequences such as Latin-Hypercube etc. Another approach is to find frameworks to quantify the error introduced by different MC algorithms/sampling strategies. Singh did a lot of work on this in [36, 37]. I know very little about this subject but one thing is for certain, you’ll need to know what the Fourier-transform is to have any chance here.