2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-3DPU.7
Paper Title UNSUPERVISED LEARNING OF VISUAL ODOMETRY USING DIRECT MOTION MODELING
Authors Silviu Andrei, Illinois Institute of Technology / amazon.com, United States; Gady Agam, Illinois Institute of Technology, United States
SessionSS-3DPU: Special Session: 3D Visual Perception and Understanding
LocationArea B
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine Learning for 3D Image and Video Processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Data for supervised learning of ego-motion and depth from video is scarce and expensive to produce. Subsequently, recent work has focused on unsupervised learning methods and achieved remarkable results. Many unsupervised approaches rely on single-view predicted depth and so ignore motion information. Some unsupervised methods incorporate motion information indirectly by designing the depth prediction network as an RNN. However, none of the existing methods make direct use of multiple frames when predicting depth, which are readily available in videos. In this work, we show that it is possible to achieve superior pose prediction results by modeling motion more directly. Our method uses a novel learning-based formulation for depth propagation and refinement which warps predicted depth maps forward from the current frame onto the next frame where it serves as a prior for predicting the next frame’s depth map. Code will be made available upon acceptance.