Accurate and occlusion-robust multi-view stereo

This paper proposes an accurate multi-view stereo method for image-based 3D reconstruction that features robustness in the presence of occlusions. The new method offers improvements in dealing with two fundamental image matching problems. The first concerns the selection of the support window model, while the second centers upon accurate visibility estimation for each pixel. The support window model is based on an approximate 3D support plane described by a depth and two per-pixel depth offsets. For the visibility estimation, the multi-view constraint is initially relaxed by generating separate support plane maps for each support image using a modified PatchMatch algorithm. Then the most likely visible support image, which represents the minimum visibility of each pixel, is extracted via a discrete Markov Random Field model and it is further augmented by parameter clustering. Once the visibility is estimated, multi-view optimization taking into account all redundant observations is conducted to achieve optimal accuracy in the 3D surface generation for both depth and surface normal estimates. Finally, multi-view consistency is utilized to eliminate any remaining observational outliers. The proposed method is experimentally evaluated using well-known Middlebury datasets, and results obtained demonstrate that it is amongst the most accurate of the methods thus far reported via the Middlebury MVS website. Moreover, the new method exhibits a high completeness rate.