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Multi-feature fusion point cloud completion network


In the real world, 3D point cloud data is generally obtained by LiDAR scanning. However, objects in the real world are occluded from each other, which will cause the point cloud scanned by LiDAR to be partially missing. In this paper, we improve PF-Net (a learning-based point cloud completion network), which is better to obtain the feature of the point cloud. Specifically, our improved network is an encoder-decoder-discriminator structure, which can directly take the missing point cloud data as input without additional preprocessing. In the encoder, we use the ALL-MLP (ALL-Multi Layer Perceptron) method to extract features from the point cloud. It combines the features obtained by each convolution in the feature extraction process, and finally sends it to the decoder. The decoder generates a prediction for the missing part of the point cloud, and the discriminator feeds back the generated result to the decoder to produce a more realistic effect. Our experiments show that the improved network has better accuracy in most categories than the state-of-the-art methods, and generates a relatively complete point cloud with achieving the purpose of complementing missing point cloud data.

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Correspondence to Xiu Chen or Yujie Li.

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This article is belongs to the Topical Collection: Special Issue on Synthetic Media on the Web Guest Editors: Huimin Lu, Xing Xu, Jože Guna, and Gautam Srivastava

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Chen, X., Li, Y. & Li, Y. Multi-feature fusion point cloud completion network. World Wide Web (2021).

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  • Point cloud
  • Feature extraction
  • Completion