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Accurate and occlusion-robust multi-view stereo.pdf

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Accurate and occlusion-robust multi-view stereo.pdf

ISPRS Journal of Photogrammetry and Remote Sensing 109 (2015) 47–61 Contents lists available at ScienceDirect ISPRS Journal of Photogrammetry and Remote Sensing journal homepage: /locate/isprsjprs Accurate and occlusion-robust multi-view stereo Zhaokun Zhu a,b,?, Christos Stamatopoulos c, Clive S. Fraser c a College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China b Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, National University of Defense Technology, Changsha 410073, China c CRCSI, Dept. of Infrastructure Engineering, University of Melbourne, Vic. 3010, Australia article info Article history: Received 23 January 2015 Received in revised form 25 August 2015 Accepted 27 August 2015 Available online 18 September 2015 Keywords: Multi-view stereo Support window model Visibility estimation PatchMatch Markov Random Field abstract 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 ?rst 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 modi?ed 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