Accurate Multi-view Stereopsis Fusing DAISY Descriptor and Scaled-Neighbourhood Patches

Abstract

In this paper, we present an efficient patch-based multi-view stereo reconstruction approach, which is designed to reconstruct accurate, dense 3D models on high-resolution image sets. Wide-baseline matching becomes more challenging due to large perspective distortions, increased occluded areas and high curvature regions that are inevitable in MVS. Correlation window measurements, which are mainly used as photometric discrepancy function, are not appropriate for wide-baseline matching. We introduce DAISY descriptor for photo-consistency optimization of each new patch, which makes our algorithm robust on distortion, occlusion and edge regions against many other photometric constraints. Another key to the performance of Patch-based MVS is the estimation of patch normal. We estimate the initial normal of every seed patch via fitting quadrics with scaled-neighbourhood patches to handle the reconstruction of high local curvature regions. It demonstrates that our approach performs dramatically well on large-scale scene both in terms of accuracy and completeness.