2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDIVMSP-22.1
Paper Title STEREO RECTIFICATION BASED ON EPIPOLAR CONSTRAINED NEURAL NETWORK
Authors Yuxing Wang, Yawen Lu, Guoyu Lu, Rochester Institute of Technology, United States
SessionIVMSP-22: Image & Video Sensing, Modeling and Representation
LocationGather.Town
Session Time:Thursday, 10 June, 14:00 - 14:45
Presentation Time:Thursday, 10 June, 14:00 - 14:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVELI] Electronic Imaging
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract This paper proposes a novel deep neural network-based method for stereo image rectification. The neural network is mainly based on the theoretical basis of epipolar constraints from multi-view geometry and intensity constraints of images, which separately describes the relationship of the corresponding epipolar lines between a pair of image, including the epipolar-line slope and y-intercept consistency of the epipolar lines and the consistency of the corresponding intensity values between two images. Benefiting from the designed rectification framework together with a feature matching module to extract accurate corresponding key-points between views, our method is able to realize a stable and accurate stereo rectification process. Compared with classic feature-based rectification methods, our proposed method can rectify small errors, and achieve a much more accurate rectification performance. Experiments conducted on synthetic face dataset and real-world KITTI dataset demonstrate the effectiveness and robustness of the proposed method.