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
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Paper Detail

Paper IDIVMSP-11.6
Paper Title MPDNet: A 3D Missing Part Detection Network Based on Point Cloud Segmentation
Authors Zhaoxin Fan, Renmin University of China, China; Hongyan Liu, Tsinghua University, China; Jun He, Min Zhang, Xiaoyong Du, Renmin University of China, China
SessionIVMSP-11: Image & Video Segmentation
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 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
Abstract Utilizing computer vision technologies for machinery missing part detection has been a hot research topic recently. Most of existing methods take images as input and utilize 2D object detection pipelines for detecting fault regions. However, 2D models can’t handle the situation when occlusion exists. Therefore, we propose MPDNet, a model exploits 3D point cloud pairs as input for missing part detection. In MPDNet, the missing part detection problem is transformed into a binary segmentation problem. The key idea is that difference between two point clouds can be fully perceived if they share the same encoder. We firstly propose a shared encode and abnormal lift module to find and enlarge difference between the target point cloud to be diagnosed and its corresponding predefined source point cloud . Then an attention based decoder is proposed to segment the source point cloud into two clusters: points that are missing in the target point cloud and points that are preserved in the target point cloud. What's more, a point cloud construction module is proposed as an auxiliary task to help the shared encoder to learn more discriminative features. Experiments on both synthetic and real world datasets have demonstrated the effectiveness of MPDNet.