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Conventional methods most often mesh the domain, which is both challenging and expensive for geometry with fine details or imperfections (holes, self-intersections,\n            <jats:italic toggle=\"yes\">etc.<\/jats:italic>\n            ). In contrast, grid-free Monte Carlo methods avoid mesh generation entirely, and instead just evaluate closest point queries. They hence do not discretize space, time, nor even function spaces, and provide the exact solution (in expectation) even on extremely challenging models. More broadly, they share many benefits with Monte Carlo methods from photorealistic rendering: excellent scaling, trivial parallel implementation, view-dependent evaluation, and the ability to work with any kind of geometry (including implicit or procedural descriptions). We develop a complete \"black box\" solver that encompasses integration, variance reduction, and visualization, and explore how it can be used for various geometry processing tasks. In particular, we consider several fundamental linear elliptic PDEs with constant coefficients on solid regions of R\n            <jats:sup>\n              <jats:italic toggle=\"yes\">n.<\/jats:italic>\n            <\/jats:sup>\n            Overall we find that Monte Carlo methods significantly broaden the horizons of geometry processing, since they easily handle problems of size and complexity that are essentially hopeless for conventional methods.\n          <\/jats:p>","DOI":"10.1145\/3386569.3392374","type":"journal-article","created":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T11:44:27Z","timestamp":1597232667000},"update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":88,"title":["Monte Carlo geometry processing"],"prefix":"10.1145","volume":"39","author":[{"given":"Rohan","family":"Sawhney","sequence":"first","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"Keenan","family":"Crane","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}]}],"member":"320","published-online":{"date-parts":[[2020,8,12]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.crma.2013.02.010"},{"key":"e_1_2_2_2_1","volume-title":"Stratified sampling of 2-manifolds. 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