Is there ongoing research on Markov Chain Monte Carlo algorithms (other than Metropolis-Hastings) for use in ray tracing? Metropolis Light Transport is an application of the Metropolis-Hastings algorithm. Its variants differ only in the used "mutation strategy".

Is there ongoing research on other Markov Chain Monte Carlo algorithms (with faster convergence rates) for use in ray tracing?

If so, I'm interested in any paper.
Overview

Here is a short overview of the most used space representations, MLT variants and mutation strategies for these MLT variants. As you can see, there are quite some papers dating back to 2017 (e.g., three papers explore combining the Path Space and the Primary Sample Space by jumping back and forth between the two).

Path Space (PS) representation, Metropolis Light Transport (MLT)


Veach E., Guibas L. J.: Metropolis Light Transport. In Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques (New York, NY, USA, 1997), SIGGRAPH ’97, ACM Press/Addison-Wesley Publishing Co.
Veach E.: Robust monte carlo methods for light transport simulation. Stanford University PhD thesis, 1997.
Van de Woestijne J., Frederickx R., Billen N., Dutré P.: Temporal Coherence for Metropolis Light Transport. Eurographics Symposium on Rendering - Experimental Ideas &amp; Implementations 2017.


[Example implementation available in Mitsuba]

Primary Sample Space (PSS) representation, PSSMLT


Kelemen C., Szirmay-Kalos L., Antal G., Csonka F.: A Simple and Robust Mutation Strategy for the Metropolis Light Transport Algorithm.


Extended Primary Sample Space (EPSS) representation, Multiplexed MLT (MMLT)


Hachisuka T., Kaplanyan A. S., Dachsbacher C.: Multiplexed Metropolis Light Transport. ACM Trans. Graph. 33, 4 (July 2014).


Note that MMLT improves upon PSSMLT. 

[Example implementation available in pbrt-v3]

Manifold Exploration MLT (MEMLT)


Jakob W., Marschner S.: Manifold Exploration: A Markov Chain Monte Carlo Technique for Rendering Scenes with Difficult Specular Transport. ACM Trans. Graph. 31, 4 (July 2012).
Jakob W.: Light Transport On Path-Space Manifolds. Cornell University PhD thesis, 2013.


[Example implementation available in Mitsuba]

Half Vector Space representation, Half Vector Space Light Transport (HSLT)


Kaplanyan A. S., Hanika J., Dachsbacher C.: The Natural-constraint Representation of the Path Space for Efficient Light Transport Simulation. ACM Trans. Graph. 33, 4 (July 2014).
Hanika J., Kaplanyan A., Dachsbacher C.: Improved half vector space light transport. In Computer Graphics Forum (2015), vol. 34, Wiley Online Library.


Note that HSLT is more general than MEMLT: MEMLT is an optional add-on mutation for MLT, whereas HSLT replaces the complete Path Space representation.

Gradient Domain representation, GDMLT


Lehtinen J., Karras T., Laine S., Aittala M., Durand F., Aila T.: Gradient-domain Metropolis Light Transport. ACM Trans. Graph. 32, 4 (July 2013).
Manzi M., Rousselle F., Kettunen M., Lehtinen J., Zwicker M.: Improved Sampling for Gradient-domain Metropolis Light Transport. ACM Trans. Graph. 33, 6 (Nov. 2014).


Note that there are variants for path tracing and bidirectional path tracing exploiting the Gradient Domain representation as well.

Path Space &lt;-> Primary Sample Space representations

Reversible Jump MLT (RJMLT)


Bitterli B.: Informed Choices In Primary Sample Space. Master’s thesis, Swiss Federal Institute of Technology Zurich, 2015.
Bitterli B., Jakob W., Novák J., Jarosz W.: Reversible Jump Metropolis Light Transport Using Inverse Mappings. ACM Transactions on Graphics 37, 1 (Oct. 2017).


Charted MLT (CMLT)


Pantaleoni J.: Charted Metropolis Light Transport. ACM Trans. Graph. 36, 4 (July 2017).


Fusing State Spaces


Otsu H., Kaplanyan A. S., Hanika J., Dachsbacher C., Hachisuka T.: Fusing State Spaces for Markov Chain Monte Carlo Rendering. ACM Transactions on Graphics (Proc. of SIGGRAPH) 36, 4 (2017).


Genetic operators in Path Space and Extended Primary Sample Space, Genetic MLT (GMLT)


Delabie M.: Genetic Operators in Metropolis Light Transport. Master’s thesis, KU Leuven, Belgium, 2018.


Note that the Path Space is represented via 3D path vertex positions instead of directions to facilitate crossovers (compared to MLT).

Post Scriptum


  Its variants differ only in the used "mutation strategy".


Different mutation strategies and space representations are used. Furthermore, GMLT replaces and extends Markov states representing single paths to complete path populations (Evolutionary Monte Carlo, Evolutionary Markov Chain Monte Carlo &lt;> Markov Chain Monte Carlo).

Some references to Evolutionary Monte Carlo:


Liang F., Wong W. H.: Evolutionary Monte Carlo: Applications to Cp Model Sampling and Change Point Problem. STATISTICA SINICA 10 (2000).
Liang F., Wong W. H.: Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models. Journal of the American Statistical Association 96, 454 (2001).