Deep neural network for handcrafted cost-based multi-view stereo

Yoonbae Jeon; In Kyu Park

doi:10.1117/12.2591008

13 March 2021 Deep neural network for handcrafted cost-based multi-view stereo

Yoonbae Jeon, In Kyu Park

Author Affiliations +

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 117660T (2021) https://doi.org/10.1117/12.2591008
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

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.

Citation Download Citation

Yoonbae Jeon and In Kyu Park "Deep neural network for handcrafted cost-based multi-view stereo", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 117660T (13 March 2021); https://doi.org/10.1117/12.2591008

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