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 ...
Deep learning-based point cloud denoising algorithms have shown excellent denoising performance on simple CAD models. However, these algorithms suffer from issues including over-smoothing or shrinkage and low efficiency when applied on density uneven, incomplete, various types of noise and complex plant ...
This paper presents a denoising approach for three-dimensional point cloud data of annular forgings based on Grassmann manifold and density clustering (GDAD). First, within the Grassmann manifold, the core points for density clustering are determined using density parameters. Second, density clustering ...
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 ...
Addressing this issue, this study proposes an adaptive kernel approach based on local density and global statistics (AKA-LDGS). This method constructs the overall framework for point cloud denoising using Bayesian estimation theory. It dynamically sets the prior probabilities of real and nois...
The resulting algorithm relies much less on the input points to have a good initial distribution (neither uniform nor close to the target density distribution) than many previous refinement‐based methods. We demonstrate the simplicity and effectiveness of our algorithm with point clouds sampled from ...
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...
All scans include a large number of noise points; denoising procedures are presented in detail. Density values are considered to be a minor source of error in the method if applied to stem segments, as comparison to ground truth data reveals that prediction errors for the tree volumes are in...
point cloud datasets that we used in our topographic and geospatial modeling research. Using the published literature, we identified sources of point density variations and issues indicated or caused by these variations. Lastly, we discuss the reduction in point density variations using decimations, ...
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 based on ...