Home → Magazine Archive → November 2017 (Vol. 60, No. 11) → The Heat Method For Distance Computation → Abstract

The Heat Method For Distance Computation

By Keenan Crane, Clarisse Weischedel, Max Wardetzky

Communications of the ACM, Vol. 60 No. 11, Pages 90-99

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We introduce the heat method for solving the single- or multiple-source shortest path problem on both flat and curved domains. A key insight is that distance computation can be split into two stages: first find the direction along which distance is increasing, then compute the distance itself. The heat method is robust, efficient, and simple to implement since it is based on solving a pair of standard sparse linear systems. These systems can be factored once and subsequently solved in near-linear time, substantially reducing amortized cost. Real-world performance is an order of magnitude faster than state-of-the-art methods, while maintaining a comparable level of accuracy. The method can be applied in any dimension, and on any domain that admits a gradient and inner product—including regular grids, triangle meshes, and point clouds. Numerical evidence indicates that the method converges to the exact distance in the limit of refinement; we also explore smoothed approximations of distance suitable for applications where greater regularity is desired.

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1. Introduction

The multiple-source shortest path problem seeks the distance from each point of a domain to the closest point within a given subset; different versions of this problem are fundamental to a wide array of problems across computer science and computational mathematics. Solutions date back at least to Dantzig's work on linear programs35; typically the problem is formulated in terms of a weighted graph, as in Dijkstra's algorithm. Often, however, one wishes to capture the distance on a continuous domain; a key example is illustrated in Figure 1 (left) where the graph distance will overestimate the straight-line Euclidean distance, no matter how fine the grid becomes. In 2D, an important development was the formulation of "exact" algorithms, where paths can cut through the faces of a triangulation8, 27; a great deal of subsequent work has focused on making these O(n2) algorithms practical for large datasets.40,46 However, for problems in data analysis and scientific computing it is not clear that the cost and complexity of exact algorithms are always well-justified, since the triangulation itself is only an approximation of the true domain (see Figure 4).

Figure 1. In contrast to algorithms that compute shortest paths along a graph (left), the heat method computes the distance to points on a continuous, curved domain (right). A key advantage of this method is that it is based on sparse linear equations that can be efficiently prefactored, leading to dramatically reduced amortized cost.

A very different approach is to formulate the problem in terms of partial differential equations (PDEs), where domain approximation error can be understood via, for example, traditional finite element analysis. However, the particular choice of continuous formulation has a substantial impact on computation. The heat method was inspired by S.R.S. Varadhan's classic result in differential geometry42 relating heat diffusion and geodesic distance, which measures the length along shortest and straightest curves through the domain rather than straight lines through space. Our key observation is that one can decompose distance computation into two stages: first determine the direction along which distance increases, then recover the distance itself. Moreover, since each stage amounts to a standard problem in numerical linear algebra, one can leverage existing algorithms and software to improve the efficiency and robustness of distance computation. Although this approach can in principle be used in the context of graph distance, its real utility lies in approximating the distance on continuous, curved domains. This approach has proven effective for a diverse range of applications in computational neuroscience, geometric modeling, medical imaging, computational design, and machine learning (Section 2), and has recently inspired more accurate variations of our original method.3


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