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 ID3D-4.3
Paper Title SELF-SUPERVISED DEPTH COMPLETION VIA ADAPTIVE SAMPLING AND RELATIVE CONSISTENCY
Authors Zidong Cao, Ang Li, Zejian Yuan, Xi'an Jiaotong University, China
Session3D-4: 3D Image and Video Processing
LocationArea J
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Three-Dimensional Image and Video Processing: 3D Image and Video Analysis and Compression
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Depth sensing is crucial for many computer vision applications. Commodity-level RGB-D cameras are often unable to sense depth in distant, reflective and transparent regions, resulting in large missing areas. As the acquisition of depth annotations in missing areas is tedious, we propose a self-supervised method for the task of completing depth values of missing areas. Specifically, we sample the incomplete raw depth map via an adaptive sampling strategy to generate a more incomplete depth map as the input and use the raw depth map as the training label. To enable the network to propagate long-range depth information to fill large invalid areas, we further propose a relative consistency loss during training. Experiments validate the effectiveness of our self-supervised method, which outperforms previous unsupervised methods and even can compete with some supervised methods. Our code is available at https://github.com/caozidong/Depth-Completion.