Learning to Fuse Proposals from Multiple
Scanline Optimizations in Semi-Global Matching
Abstract. Semi-Global Matching (SGM) uses an aggregation scheme
to combine costs from multiple 1D scanline optimizations that tends to
hurt its accuracy in difficult scenarios. We propose replacing this aggregation scheme with a new learning-based method that fuses disparity
proposals estimated using scanline optimization. Our proposed SGMForest algorithm solves this problem using per-pixel classification. SGMForest currently ranks 1st on the ETH3D stereo benchmark and is ranked
competitively on the Middlebury 2014 and KITTI 2015 benchmarks. It
consistently outperforms SGM in challenging settings and under difficult
training protocols that demonstrate robust generalization, while adding
only a small computational overhead to SGM.