Zaman, F.; Wong, Y.P.; Ng, B.Y. Density-based denoising of point cloud. In Proceedings of the 9th International Conference on Robotic, Vision, Signal, Processing and Power Applications; Springer: Singapore, 2017.Density-based Denoising of Point Cloud. Zaman F,Wong Y P,Ng B Y. 9th ...
(ISPRS)Effective point cloud denoising is critical in 3D plant phenotyping applications, which reduces interference in subsequent algorithms and improves the accuracy of plant phenotypes measurement. Deep learning-based point cloud denoising algorithms have shown excellent denoising performance on simple CAD ...
Simulation experiments on annular forgings demonstrate that GDAD effectively eliminates edge noise in annular forgings and performs well in denoising point-cloud models with varying levels of noise intensity. 展开 关键词: annular forgings Grassmann manifold point cloud denoising density clustering search ...
To overcome this deficiency, a density-based point\ncloud denoising method is presented to remove outliers and noisy points. First,\nparticle-swam optimization technique is employed for automatically\napproximating optimal bandwidth of multivariate kernel density estimation to\nensure the robust ...
For this reason, we employed the airborne point cloud with high density as the reference data to evaluate the signal extraction algorithm presented in this work [39], which provides an increasingly objective benchmark to quantify denoising accuracy. For supplementary validation, the digital terrain ...
Figure 1. Normals of a point cloud of leaf and non-leaf materials. The arrows show the normals of the point cloud. It can be seen that the normal change rate theoretically is higher in leaves compared to more homogeneous woody segments, like branches and stems. The normal differences of...
This method constructs the overall framework for point cloud denoising using Bayesian estimation theory. It dynamically sets the prior probabilities of real and noise points according to the spatial function relationship, which varies with the distance from the points to the center of the LiD...
First, the histogram-based successive denoising method was used as a coarse denoising process to remove distant noise and part of the sparse noise, thereby increasing the fault tolerance of the subsequent steps. Second, a rotatable ellipse that adaptively corrects the direction and shape base...
The required wall-clock time of each frame is approximately 200 ms, with relative time durations of 1% to read the frame, 1% to index it, 3% to create the binary mask, 78% for the denoising operations, 16% for the application of the watershed analysis and 1% for the blob analysis. ...
It is widely applied to image denoising [16], image deblurring [17], image inpainting [18], super-resolution [19] and image fusion [20]. Yang and Li [21] first applied the sparse representation theory to image fusion field and also proposed a multi-focus image fusion method with an MST...