Hi everyone,
We will have our first theory seminar of the semester this Friday 2-3pm in CS 3310.
I will be presenting on âA Near Linear Time Algorithm for the Chamfer Distance.â
Abstract:
I will describe a fast algorithm to approximate the Chamfer distance, a commonly used proxy for the more computationally demanding Earth-Mover (Optimal Transport) Distance. For any two point sets A and B of size n, the Chamfer distance from A
to B is defined as CH(A, B) = sum over x in A of d(x, B), where d(x, B) is the minimum distance from x to any point in B. In other words, it is the sum of nearest neighbor distances from A to B. The Chamfer distance is a popular measure of dissimilarity between
point clouds, used in many machine learning, computer vision, and graphics applications, and admits a straightforward O(n^2)-time brute force algorithm. However, the quadratic dependence on n in the running time makes the naive approach intractable for large
datasets. I will present a (1+epsilon)-approximate algorithm for estimating the Chamfer distance with a near-linear running time. Our experiments demonstrate that it is both accurate and fast on large high-dimensional datasets.
Please let me know if you are interested in giving a talk at some point in the semester (students are welcome as well!).
Next week Manolis will be giving a talk.
Best,
Sandeep
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