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.

翻译：激光雷达（LiDAR，光探测与测距）技术在自然与人工环境捕获中已广泛应用于众多场景。近期电光工程的进步使得以更高脉冲重复频率（PRF）获取激光回波成为可能，从而大幅提升了三维点云的密度。传统低PRF技术具有足够大的单脉冲空中（SPIA）区域，可避免接收端脉冲对间的失配。新型多脉冲空中（MPIA）技术则保证了单条航线的多种工作距离范围且无盲区。然而，该技术的缺陷在于，邻近地形点的大气回波投影更接近同一脉冲空中区域，易与目标物体发生交叠。此类噪声特性降低了场景的感知质量，并促使新型噪声滤波神经网络的发展，因为现有滤波器效果显著不足。我们提出了一种名为噪声关注注意力网络（NSANet）的新型双注意力噪声滤波神经网络，该网络利用物理先验与局部空间注意力进行噪声滤波。研究受特征整合理论与注意力参与理论两大心理学理论的启发，旨在证明注意力在计算机视觉编解码阶段的作用。NSANet的实验结果显示出对注意力参与理论的偏好，且相较于当前最先进的深度卷积神经网络噪声滤波器，其性能得到了提升。