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In mathematics and its applications, the **signed distance function** (or **oriented distance function**) of a set Ω in a metric space determines the distance of a given point *x* from the boundary of Ω, with the sign determined by whether *x* is in Ω. The function has positive values at points *x* inside Ω, it decreases in value as *x* approaches the boundary of Ω where the signed distance function is zero, and it takes negative values outside of Ω.^{[1]} However, the alternative convention is also sometimes taken instead (i.e., negative inside Ω and positive outside).^{[2]}

If Ω is a subset of a metric space, *X*, with metric, *d*, then the *signed distance function*, *f*, is defined by

where denotes the boundary of . For any ,

where inf denotes the infimum.

If Ω is a subset of the Euclidean space **R**^{n} with piecewise smooth boundary, then the signed distance function is differentiable almost everywhere, and its gradient satisfies the eikonal equation

If the boundary of Ω is *C*^{k} for *k* ≥ 2 (see Differentiability classes) then *d* is *C*^{k} on points sufficiently close to the boundary of Ω.^{[3]} In particular, * on* the boundary

where *N* is the inward normal vector field. The signed distance function is thus a differentiable extension of the normal vector field. In particular, the Hessian of the signed distance function on the boundary of Ω gives the Weingarten map.

If, further, Γ is a region sufficiently close to the boundary of Ω that *f* is twice continuously differentiable on it, then there is an explicit formula involving the Weingarten map *W*_{x} for the Jacobian of changing variables in terms of the signed distance function and nearest boundary point. Specifically, if *T*(*∂*Ω, *μ*) is the set of points within distance *μ* of the boundary of Ω (i.e. the tubular neighbourhood of radius *μ*), and *g* is an absolutely integrable function on Γ, then

where det denotes the determinant and *dS*_{u} indicates that we are taking the surface integral.^{[4]}

Algorithms for calculating the signed distance function include the efficient fast marching method, fast sweeping method^{[5]} and the more general level-set method.

Signed distance functions are applied, for example, in real-time rendering^{[6]} and computer vision.^{[7]}^{[8]}

A modified version of SDF was introduced as a loss function to minimise the error in interpenetration of pixels while rendering multiple objects.^{[9]} In particular, for any pixel that does not belong to an object, if it lies outside the object in rendition, no penalty is imposed; if it does, a positive value proportional to its distance inside the object is imposed.

They have also been used in a method (advanced by Valve) to render smooth fonts at large sizes (or alternatively at high DPI) using GPU acceleration.^{[10]} Valve's method computed signed distance fields in raster space in order to avoid the computational complexity of solving the problem in the (continuous) vector space. More recently piece-wise approximation solutions have been proposed (which for example approximate a Bézier with arc splines), but even this way the computation can be too slow for real-time rendering, and it has to be assisted by grid-based discretization techniques to approximate (and cull from the computation) the distance to points that are too far away.^{[11]}

In 2020, the FOSS game engine Godot 4.0 received SDF-based real-time global illumination (SDFGI), that became a compromise between more realistic voxel-based GI and baked GI. Its core advantage is that it can be applied to infinite space, which allows developers to use it for open-world games.^{[citation needed]}

**^**Chan, T.; Zhu, W. (2005).*Level set based shape prior segmentation*. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. doi:10.1109/CVPR.2005.212.**^**Malladi, R.; Sethian, J.A.; Vemuri, B.C. (1995). "Shape modeling with front propagation: a level set approach".*IEEE Transactions on Pattern Analysis and Machine Intelligence*.**17**(2): 158–175. CiteSeerX 10.1.1.33.2443. doi:10.1109/34.368173.**^**Gilbarg 1983, Lemma 14.16.**^**Gilbarg 1983, Equation (14.98).**^**Zhao Hongkai. A fast sweeping method for eikonal equations. Mathematics of Computation, 2005, 74. Jg., Nr. 250, S. 603-627.**^**Tomas Akenine-Möller; Eric Haines; Naty Hoffman (6 August 2018).*Real-Time Rendering, Fourth Edition*. CRC Press. ISBN 978-1-351-81615-1.**^**Perera, S.; Barnes, N.; He, X.; Izadi, S.; Kohli, P.; Glocker, B. (January 2015). "Motion Segmentation of Truncated Signed Distance Function Based Volumetric Surfaces".*2015 IEEE Winter Conference on Applications of Computer Vision*: 1046–1053. doi:10.1109/WACV.2015.144. ISBN 978-1-4799-6683-7. S2CID 16811314.**^**Izadi, Shahram; Kim, David; Hilliges, Otmar; Molyneaux, David; Newcombe, Richard; Kohli, Pushmeet; Shotton, Jamie; Hodges, Steve; Freeman, Dustin (2011). "KinectFusion: Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera".*Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology*. UIST '11. New York, NY, USA: ACM: 559–568. doi:10.1145/2047196.2047270. ISBN 9781450307161. S2CID 3345516.**^**Jiang, Wen; Kolotouros, Nikos; Pavlakos, Georgios; Zhou, Xiaowei; Daniilidis, Kostas (2020-06-15). "Coherent Reconstruction of Multiple Humans from a Single Image". arXiv:2006.08586 [cs.CV].**^**Green, Chris (2007). "Improved alpha-tested magnification for vector textures and special effects".*ACM SIGGRAPH 2007 Courses on - SIGGRAPH '07*: 9. CiteSeerX 10.1.1.170.9418. doi:10.1145/1281500.1281665. ISBN 9781450318235. S2CID 7479538.**^***GLyphy: high-quality glyph rendering using OpenGL ES2 shaders [linux.conf.au 2014]*.*YouTube*. Archived from the original on 2021-12-11.

- Stanley J. Osher and Ronald P. Fedkiw (2003).
*Level Set Methods and Dynamic Implicit Surfaces*. Springer. ISBN 9780387227467. - Gilbarg, D.; Trudinger, N. S. (1983).
*Elliptic Partial Differential Equations of Second Order*. Grundlehren der mathematischen Wissenschaften.**224**(2nd ed.). Springer-Verlag. (or the Appendix of the 1977 1st ed.)