The generation of dense and accurate point clouds from airborne light detection and ranging (LiDAR) waveform data is crucial to forest inventory. This work proposes a deconvolution method with: 1) an automatic stopping criterion to differentiate near-adjacent targets and 2) an iterative false subwaveform removal algorithm to remove outliers caused by noise. Synthetic waveforms with different overlap rates were processed using the proposed method, the Gaussian decomposition (GD) method, and the Richardson Lucy (RL) deconvolution method. Results showed that: 1) the number of subwaveforms detected by the proposed method is 9% higher than that of the RL and 20% higher than that of the GD when the overlap rates are larger than 0.6 and 2) the proposed method is of the smallest ground and peak distance errors. Results of the indoor experiment also show that the proposed method is superior in finding near targets meanwhile leading to small ground and peak distance error. Furthermore, the proposed method was tested by airborne waveforms from the Dagujia forest farm. The point cloud density acquired by the proposed method is 3% and 35% higher than that by the RL and GD method. Fewer outliers are produced by the proposed method. The number of individual trees extracted from the proposed point clouds is 22%, 51%, and 57% greater than those extracted from the RL, the GD, and the reference point clouds using the canopy height model-based method. Best individual tree extraction result is produced by the proposed method, especially for an area with small trees.

The modeling of laser transmission characteristics in complex seawater is fundamental for bathymetric and oceanographic laser detection systems. Because the factors affecting laser transmission in seawater are independent of one another, firstly, in this paper, a Monte Carlo model of laser propagation in seawaters with suspended matter was established to study the influence of suspended matter with specific radius on the underwater laser transmission. Secondly, the influence of transmission depth and the number of photons on the laser extinction coefficients of seawater containing different concentrations of suspended matter were analyzed, respectively. Thirdly, the relationships between maximum transmission depth, the number of initial photons, and the concentrations were built and verified by simulations. Lastly, an experimental platform was set up and experiments were carried out to verify the Monte Carlo model and the relationships. Results show that (1) both the minimum initial photon number and maximum transmission depth depend exponentially on the concentrations of the suspended matter; (2) the extinction coefficients obtained by the Monte Carlo model and those obtained by experiments are consistent. The absolute values of the differences are less than 0.028 m⁻¹, implying that (1) the proposed Monte Carlo model is effective for simulating laser propagation in seawaters with suspended matter; (2) the established relationships between maximum transmission depth, the minimum initial photon number, and the concentrations of suspended matter have better accuracies, which are valuable for the simulations on attenuation of laser transmission in seawater. The method of this paper can also be extended to the study of suspended solids with other radii and improve the simulation accuracy and decrease simulation time consumption.

Point cloud processing is a highly challenging task in 3D vision because it is unstructured and unordered. Recently, deep learning has been proven to be quite successful in point cloud recognition, registration, segmentation, etc. Neighborhood search operation is an important component of point cloud deep learning models, and directly affects the performance of the model. In this paper, we propose a learnable neighborhood search method. This method adaptively chooses an appropriate search method based on the characteristics of each point, thus avoiding the disadvantage of selecting the search method manually. We validate the proposed methods on ModelNet40 dataset and ShapeNetPart dataset, and all the chosen models achieved a performance improvement with a maximum improvement of 1.1%. The proposed method is a plug-and-play technique and can be easily integrated into existing methods.

The ranging performance of time-of-flight (ToF) cameras is mainly influenced by the signal harmonics, measurement noise and number of repeated integrations. However, current depth error correction methods lack a synthetic-factor model to elucidate the error generation mechanism deducing the image quality degradation of the ToF camera. To overcome the shortcomings of the current methods, a depth error model, combining the wiggling error, temporal error and integration time-related error, was proposed to achieve single-pixel high-precision distance correction based on the charge transfer principle. A vertical center-pixel depth error calibration method was used to obtain the real distance deviation and validate the depth error model under various distances and integration times. Based on the probability distribution of all-pixel depth errors, a segmented pixel region correction method was advanced to suppress the randomness error of each pixel. Based on a commercial ToF camera, a series of depth error correction experiments was carried out. The experimental results show that the distance deviations of all-pixel imaging are mainly centered on 0.26 ± 3 mm in the range of 2.6 m, and its integration time is 4 ms. The proposed depth error model provides an effective way to enhance the ToF camera ranging accuracy, serving a wider audience of researchers and industrial practitioners.

Accurate and robust visual localization is essential for autonomous lunar landing. This study presents a new crater-based method that addresses challenges posed by environmental uncertainties such as camera pose deviations, the number of craters within the scene, and the image brightness. Our method combines crater Geohash encoding for efficient database retrieval with an improved principal component analysis (PCA) for crater detection. The detected craters are ranked, retaining those with fewer but more accurate detections to meet localization requirements. Crucially, we introduce a consistency matching technique that exploits the linear relationship between position shifts and pixel offsets, enhancing both localization accuracy and computational efficiency. Experimental results across diverse scenes and simulation conditions demonstrate 100% matching accuracy with an average matching time under 0.8 s. Reprojection errors remain below 3 px, significantly outperforming methods like triangle similarity matching (TSM) and direct matching (DM). This validates the proposed method’s high precision and stability for near real-time lunar localization.