# NSANet: Noise Seeking Attention Network

arXiv: https://arxiv.org/abs/2302.13392 · HTML: https://mlsanity.com/paper/2302.13392/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 71 references that are in the map.
- First posted on arXiv 26 Feb 2023.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds](https://mlsanity.com/paper/1901.01060/) (2019)
- [Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning](https://mlsanity.com/paper/1904.07615/) (2019)
- [Differentiable Manifold Reconstruction for Point Cloud Denoising](https://mlsanity.com/paper/2007.13551/) (2020)
- [Pointfilter: Point Cloud Filtering via Encoder-Decoder Modeling](https://mlsanity.com/paper/2002.05968/) (2020)
- [Learning Graph-Convolutional Representations for Point Cloud Denoising](https://mlsanity.com/paper/2007.02578/) (2020)
- [Feature Graph Learning for 3D Point Cloud Denoising](https://mlsanity.com/paper/1907.09138/) (2019)
- [3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation](https://mlsanity.com/paper/1904.04427/) (2019)
- [Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance](https://mlsanity.com/paper/2003.08355/) (2020)
- [Graph-based denoising for time-varying point clouds](https://mlsanity.com/paper/1511.04902/) (2015)
- [Density-based Denoising of Point Cloud](https://mlsanity.com/paper/1602.05312/) (2016)
- [Blending Diverse Physical Priors with Neural Networks](https://mlsanity.com/paper/1910.00201/) (2019)
- [Deep Manifold Prior](https://mlsanity.com/paper/2004.04242/) (2020)
- [MaskNet: A Fully-Convolutional Network to Estimate Inlier Points](https://mlsanity.com/paper/2010.09185/) (2020)
- [Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction](https://mlsanity.com/paper/2102.13391/) (2021)
- [Non-Local Part-Aware Point Cloud Denoising](https://mlsanity.com/paper/2003.06631/) (2020)
- [3D Point Cloud Denoising via Bipartite Graph Approximation and Reweighted Graph Laplacian](https://mlsanity.com/paper/1812.07711/) (2018)
- [Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation](https://mlsanity.com/paper/1804.10831/) (2018)

## 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.