A DAISY descriptor based multi-view stereo method for large-scale scenes

• An improved patch based MVS method by a novel photometric discrepancy function.
• Proposed a new corresponding point matching method based on the DAISY descriptor.
• Proposed a new photometric discrepancy function based on DAISY descriptor.

Normalized cross-correlation (NCC) has been widely used as the matching cost function in multi-view stereo methods. However, NCC is vulnerable in the occlusion area and edge region of large-scale scenes because of color distortion and illumination changes. To alleviate the above problems, we present an improved patch based multi-view stereo method by introducing a photometric discrepancy function based on DAISY descriptor. In the patch extraction stage, a new corresponding point matching method based on the DAISY descriptor is proposed and the epipolar constraint is used to filter mismatched points. In the patch optimization stage, a photometric discrepancy function based on DAISY descriptor is proposed to measure the photo-consistency among reconstructed patches to identify reliable patches. Finally, dense patches are obtained by expanding sparse patches with global visibility information and patch optimization. Experimental results show that the proposed algorithm obtains better reconstruction results in occlusion and edge regions of large-scale scenes.

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