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Point clouds obtained from 3D scanners are often noisy and cannot be directly used for subsequent high-level tasks. In this article, we propose a novel point cloud optimization method capable of denoising and homogenizing point clouds. Our idea is based on the assumption that the noise is generally much smaller than the effective signal. We perform noise perturbation on the noisy point cloud to get a new noisy point cloud, called self-variation point cloud. The noisy point cloud and self-variation point cloud have different noise distribution, but the same point cloud distribution. We compute the potential commonality between two noisy point clouds to obtain a clean point cloud. To implement our idea, we propose a Self-Variation Capture Network (SVCNet). We perturb the point cloud features in the latent space to obtain self-variation feature vectors, and capture the commonality between two noisy feature vectors through the feature aggregation and averaging. In addition, an edge constraint module is introduced to suppress low-pass effects during denoising. Our denoising method does not take into account the noise characteristics, and can filter the drift noise located on the underlying surface, resulting in a uniform distribution of the generated point cloud. The experimental results show that our algorithm outperforms the current state-of-the-art algorithms, especially in generating more uniform point clouds. In addition, extended experiments demonstrate the potential of our algorithm for point clouds upsampling.

Vehicle detection in infrared aerial images is vital for both military and civilian applications, as infrared imaging remains effective under low-light conditions and various adverse weather scenarios. However, the longer wavelengths of long-wave infrared, compared to visible light, make diffraction more noticeable, leading to low-frequency degradation of vehicle information. Thermal radiation from the environment and optical system leads to higher noise in infrared images. Additionally, atmospheric transport models for various weather conditions can degrade infrared images to different extents. These factors lead to a reduced signal-to-noise ratio (SNR), which complicates the extraction of clear features. To overcome these challenges, we propose the dual-tuning network (DTNet), an advanced framework for vehicle detection in infrared aerial images, developed based on the mechanisms of infrared imaging. Specifically, the core component of DTNet is the dual-tuning block (DTBlock), which works alongside the dynamic guided filtering module (DGFM) and the point spread recovery module (PSRM) for feature extraction. DTBlock decomposes feature maps into low- and high-frequency components with learnable low-pass filters. DGFM eliminates disturbance from the optical system and background thermal radiation in the high-frequency component of feature maps, while preserving the details and texture of vehicles. PSRM aggregates vehicle features in the low-frequency component of feature maps, which is proposed with reference to diffraction and atmospheric models. The concept of dual-tuning refers to enhancing the signal and suppressing interference in the low- and high-frequency parts, respectively. Experimental results on the DroneVehicle public dataset for infrared vehicle detection indicate that our proposed approach achieves state-of-the-art (SOTA) performance. Moreover, extensive ablation studies confirm the superior capability of our DTNet in robust feature extraction from infrared images.