{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T11:40:29Z","timestamp":1786621229398,"version":"3.56.0"},"reference-count":26,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computer Graphics Forum"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Fast and differentiable solvers for anisotropic and asymmetric distance fields are a key primitive in geometry processing, enabling gradient\u2010based optimization over metrics, drift fields, and downstream objectives that depend on geodesic distances and geodesics. We present a differentiable Eikonal solver for Randers\u2013Finsler metrics on Cartesian grids that combines the efficiency of a GPU\u2010friendly column\/row fast sweeping with exact gradients obtained by implicit differentiation. Our forward pass uses local one\u2010 and two\u2010point upwind updates selected by a causality\u2010valid stencil; the backward pass exploits the induced arrival\u2010time ordering to solve the adjoint system via a single reverse\u2010time back\u2010substitution, avoiding unrolling (i.e., recording and differentiating through every solver iteration) and substantially reducing memory and runtime. We derive closed\u2010form derivatives of the discrete updates with respect to arrival times and Randers parameters, and we enforce metric feasibility with differentiable projections that guarantee positive definiteness and valid drift magnitude. Although stencil selection is piecewise\u2010smooth, we show gradients are stable under small perturbations and match finite differences away from stencil boundaries. We demonstrate accurate forward solutions and enable inverse problems such as recovering spatially varying anisotropic metrics and drift fields from sparse arrival\u2010time supervision. Finally, we apply the method to learning data\u2010driven spread models on real wildfire perimeters, illustrating scalability and the practical utility of differentiable Randers distance fields.<\/jats:p>","DOI":"10.1111\/cgf.70489","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T11:23:51Z","timestamp":1786620231000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Differentiable Randers\u2010Finsler Eikonal Solvers"],"prefix":"10.1111","author":[{"given":"Barak","family":"Gahtan","sequence":"first","affiliation":[{"name":"Department of Computer Science Technion \u2010 Israel Institute of Technology  Haifa Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Shpund","sequence":"additional","affiliation":[{"name":"The Hebrew University of Jerusalem  Jerusalem Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex 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