Abstract
Rigid Point Cloud Registration (PCReg) refers to the
problem of finding the rigid transformation between two
sets of point clouds. This problem is particularly important due to the advances in new 3D sensing hardware, and
it is challenging because neither the correspondence nor
the transformation parameters are known. Traditional local PCReg methods (e.g., ICP) rely on local optimization
algorithms, which can get trapped in bad local minima in
the presence of noise, outliers, bad initializations, etc. To
alleviate these issues, this paper proposes Inverse Composition Discriminative Optimization (ICDO), an extension of
Discriminative Optimization (DO), which learns a sequence
of update steps from synthetic training data that search the
parameter space for an improved solution. Unlike DO,
ICDO is object-independent and generalizes even to unseen shapes. We evaluated ICDO on both synthetic and real
data, and show that ICDO can match the speed and outperform the accuracy of state-of-the-art PCReg algorithms