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NSANet: Noise Seeking Attention Network

arXiv 2302.13392 ↗First posted 26 Feb 2023cs.CV eess.IVcited by 0

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Abstract (arXiv)

LiDAR (Light Detection and Ranging) technology has remained popular in capturing natural and built environments for numerous applications. The recent technological advancements in electro-optical engineering have aided in obtaining laser returns at a higher pulse repetition frequency (PRF), which considerably increased the density of the 3D point cloud. Conventional techniques with lower PRF had a single pulse-in-air (SPIA) zone, large enough to avoid a mismatch among pulse pairs at the receiver. New multiple pulses-in-air (MPIA) technology guarantees various windows of operational ranges for a single flight line and no blind zones. The disadvantage of the technology is the projection of atmospheric returns closer to the same pulse-in-air zone of adjacent terrain points likely to intersect with objects of interest. These noise properties compromise the perceived quality of the scene and encourage the development of new noise-filtering neural networks, as existing filters are significantly ineffective. We propose a novel dual-attention noise-filtering neural network called Noise Seeking Attention Network (NSANet) that uses physical priors and local spatial attention to filter noise. Our research is motivated by two psychology theories of feature integration and attention engagement to prove the role of attention in computer vision at the encoding and decoding phase. The presented results of NSANet show the inclination towards attention engagement theory and a performance boost compared to the state-of-the-art noise-filtering deep convolutional neural networks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 71 references that are in the map. First posted on arXiv 26 Feb 2023.

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23 of 71 references.

References · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  5. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  6. 2021Attention Mechanisms in Computer Vision: A Surveycited by 232
  7. 2019PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Cloudscited by 99
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  9. 2020Differentiable Manifold Reconstruction for Point Cloud Denoisingcited by 52
  10. 2020Pointfilter: Point Cloud Filtering via Encoder-Decoder Modelingcited by 50
  11. 2020Learning Graph-Convolutional Representations for Point Cloud Denoisingcited by 40
  12. 2019Feature Graph Learning for 3D Point Cloud Denoisingcited by 28
11 more
  1. 20193D Point Cloud Denoising via Deep Neural Network based Local Surface Estimationcited by 20
  2. 2020Dynamic Point Cloud Denoising via Manifold-to-Manifold Distancecited by 12
  3. 2015Graph-based denoising for time-varying point cloudscited by 11
  4. 2016Density-based Denoising of Point Cloudcited by 11
  5. 2019Blending Diverse Physical Priors with Neural Networkscited by 11
  6. 2020Deep Manifold Priorcited by 9
  7. 2020MaskNet: A Fully-Convolutional Network to Estimate Inlier Pointscited by 8
  8. 2021Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstructioncited by 7
  9. 2020Non-Local Part-Aware Point Cloud Denoisingcited by 4
  10. 20183D Point Cloud Denoising via Bipartite Graph Approximation and Reweighted Graph Laplaciancited by 3
  11. 2018Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variationcited by 2

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