TY - GEN
T1 - CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching
AU - Mehltretter, Max
AU - Heipke, Christian
N1 - Funding information: This work was supported by the German Research Foundation (DFG) as a part of the Research Training Group i.c.sens [GRK2159], the MOBILISE initiative of the Leibniz University Hannover and TU Braunschweig and by the NVIDIA Corporation with the donation of the Titan V GPU used for this research.
PY - 2019/10
Y1 - 2019/10
N2 - Due to its capability to identify erroneous disparity assignments in dense stereo matching, confidence estimation is beneficial for a wide range of applications, e.g. autonomous driving, which needs a high degree of confidence as mandatory prerequisite. Especially, the introduction of deep learning based methods resulted in an increasing popularity of this field in recent years, caused by a significantly improved accuracy. Despite this remarkable development, most of these methods rely on features learned from disparity maps only, not taking into account the corresponding 3-dimensional cost volumes. However, it was already demonstrated that with conventional methods based on hand-crafted features this additional information can be used to further increase the accuracy. In order to combine the advantages of deep learning and cost volume based features, in this paper, we propose a novel Convolutional Neural Network (CNN) architecture to directly learn features for confidence estimation from volumetric 3D data. An extensive evaluation on three datasets using three common dense stereo matching techniques demonstrates the generality and state-of-the-art accuracy of the proposed method.
AB - Due to its capability to identify erroneous disparity assignments in dense stereo matching, confidence estimation is beneficial for a wide range of applications, e.g. autonomous driving, which needs a high degree of confidence as mandatory prerequisite. Especially, the introduction of deep learning based methods resulted in an increasing popularity of this field in recent years, caused by a significantly improved accuracy. Despite this remarkable development, most of these methods rely on features learned from disparity maps only, not taking into account the corresponding 3-dimensional cost volumes. However, it was already demonstrated that with conventional methods based on hand-crafted features this additional information can be used to further increase the accuracy. In order to combine the advantages of deep learning and cost volume based features, in this paper, we propose a novel Convolutional Neural Network (CNN) architecture to directly learn features for confidence estimation from volumetric 3D data. An extensive evaluation on three datasets using three common dense stereo matching techniques demonstrates the generality and state-of-the-art accuracy of the proposed method.
KW - Confidence estimation
KW - Convolutional neural network
KW - Dense stereo matching
UR - https://www.scopus.com/pages/publications/85078929241
U2 - 10.48550/arXiv.1905.07287
DO - 10.48550/arXiv.1905.07287
M3 - Conference contribution
AN - SCOPUS:85078929241
SN - 978-1-7281-5024-6
T3 - IEEE International Conference on Computer Vision workshops
SP - 2070
EP - 2079
BT - 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE/CVF 17th International Conference on Computer Vision Workshop (ICCVW)
Y2 - 27 October 2019 through 28 October 2019
ER -