A Computer Vision Problem in Flatland
Sameer Agarwal, Erin Connelly, Annalisa Crannell, Timothy Duff, Rekha R. Thomas
TL;DR
The paper investigates when two labeled point sets in the plane share a common image under flatland projection to a line, and characterizes the loci of projection centers that realize this equivalence. It shows that two $n$-point sets have the same $\mathbb{P}^1$-image if and only if they arise from projecting a single configuration in $\mathbb{P}^3$, bridging a 2D two-view problem with a 3D reconstruction perspective. The authors develop invariant-theoretic and algebraic-geometry machinery—SL$(2)$-invariants of $n$ points in $\mathbb{P}^1$, the camera-centers variety, and explicit geometric constructions—to describe the camera loci for $n=4$ to $7$, revealing explicit loci: conics for $n=4$, intersecting conics and a degree-5 Cremona map for $n=5$, cubic curves for $n=6$, and a finite set of three centers for $n=7$. They also discuss connections to the fundamental matrix and classical Cremona transformations, and they show nonexistence for $n>7$, highlighting a sharp boundary. The work provides explicit algebraic descriptions and constructive procedures, linking projective geometry, invariant theory, and computer-vision concepts like epipolar geometry and 3D reconstruction.
Abstract
When is it possible to project two sets of labeled points lying in a pair of projective planes to the same image on a projective line? We give a complete answer to this question and describe the loci of the projection centers that enable a common image. In particular, we find that there exists a solution to this problem if and only if these two sets are themselves images of a common pointset in projective space.
