In the last decades, depth estimation from multi-view is treated as an ill-posed problem. This problem becomes severe with limited data such as sparse-view cases. However, with the presence of Convolutional Neural Network (CNN), recent learning-based depth estimation methods prove effectiveness on occluded and texture-less area where prior works still suffer on handling such issues. They utilize features from CNN layer for constructing cost volume and regress input volume with regression network. To overcome those concerns, we introduce a unique approach by combining hand-crafted and learning-based strategies. Specifically, we utilize the Normalized Cross-Correlation (NCC) cost volume, which is more robust to noise than simple L1 and L2 costs, to improve the photo-consistency between local patches. The entire construction pipeline is implemented by pyOpenCL to speed up the processing time. Finally, we employ the network that estimates depth by regressing handcrafted cost-based plane sweeping volume.
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