Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction

Yiming Qian, Yasutaka Furukawa

Abstract:

This paper proposes a novel single-image piecewise planar reconstruction technique that infers and enforces inter-plane relationships.
Our approach takes a planar reconstruction result from an existing system, then utilizes convolutional neural network (CNN) to (1) classify
if two planes are orthogonal or parallel; and 2) infer if two planes are
touching and, if so, where in the image. We formulate an optimization
problem to refine plane parameters and employ a message passing neural
network to refine plane segmentation masks by enforcing the inter-plane
relations. Our qualitative and quantitative evaluations demonstrate the
effectiveness of the proposed approach in terms of plane parameters and
segmentation accuracy.