スパースコストボリュームからのステレオマッチング学習
SCV-Stereo: Learning Stereo Matching from a Sparse Cost Volume
密なコストボリュームの冗長性に着目し、スパースなコストボリューム表現から反復的に視差を推定するCNNアーキテクチャSCV-Stereoを提案。精度と効率のトレードオフを改善した。
著者: Hengli Wang, Rui Fan, Ming Liu
分類: cs.CV, cs.RO
原文アブストラクト
Convolutional neural network (CNN)-based stereo matching approaches generally require a dense cost volume (DCV) for disparity estimation. However, generating such cost volumes is computationally-intensive and memory-consuming, hindering CNN training and inference efficiency. To address this problem, we propose SCV-Stereo, a novel CNN architecture, capable of learning dense stereo matching from sparse cost volume (SCV) representations. Our inspiration is derived from the fact that DCV representations are somewhat redundant and can be replaced with SCV representations. Benefiting from these SCV representations, our SCV-Stereo can update disparity estimations in an iterative fashion for accurate and efficient stereo matching. Extensive experiments carried out on the KITTI Stereo benchmarks demonstrate that our SCV-Stereo can significantly minimize the trade-off between accuracy and efficiency for stereo matching. Our project page is https://sites.google.com/view/scv-stereo.
