Mapping bootstrap error for bilateral smoothing on point set

Rosli, Nor Aziyatul Izni Mohd; Ramli, Ahmad

doi:10.1063/1.4887580

Cited by 12 publications

(6 citation statements)

References 1 publication

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“…The bilateral filter is a nonlinear filtration tool used for edge-preserving smoothing [ 35 ]. Due to the wiggling error caused by the Kinect sensor, the fitting surface of the data acquired by the Kinect was not smooth [ 22 ]; this could be solved by this filter.…”

Section: Methodsmentioning

confidence: 99%

Optimization of 3D Point Clouds of Oilseed Rape Plants Based on Time-of-Flight Cameras

Sun

et al. 2021

Sensors

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Three-dimensional (3D) structure is an important morphological trait of plants for describing their growth and biotic/abiotic stress responses. Various methods have been developed for obtaining 3D plant data, but the data quality and equipment costs are the main factors limiting their development. Here, we propose a method to improve the quality of 3D plant data using the time-of-flight (TOF) camera Kinect V2. A K-dimension (k-d) tree was applied to spatial topological relationships for searching points. Background noise points were then removed with a minimum oriented bounding box (MOBB) with a pass-through filter, while outliers and flying pixel points were removed based on viewpoints and surface normals. After being smoothed with the bilateral filter, the 3D plant data were registered and meshed. We adjusted the mesh patches to eliminate layered points. The results showed that the patches were closer. The average distance between the patches was 1.88 × 10−3 m, and the average angle was 17.64°, which were 54.97% and 48.33% of those values before optimization. The proposed method performed better in reducing noise and the local layered-points phenomenon, and it could help to more accurately determine 3D structure parameters from point clouds and mesh models.

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Section: Methodsmentioning

confidence: 99%

Optimization of 3D Point Clouds of Oilseed Rape Plants Based on Time-of-Flight Cameras

Sun

et al. 2021

Sensors

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“…X-Ray (Micro) Computed Tomography (CT / µCT) Barley [64] Bean plant [59,62] Cassava [59] Chickpea [64] Duckweed [57] Maize (Corn) [58,61,337] Sinningia [342] Sorghum [343] Tomato [61] Wheat [60,61,64] Other [344] Is a non-destructive imaging tool for the production of high-resolution three-dimensional (3D) images composed of two-dimensional (2D) trans-axial projections, or 'slices' , of a target specimen [345]. [15] Tomato [346] Is a non-linear filtering technique and a simple, non-iterative scheme for edge-preserving smoothing [137,142].…”

Section: Voxel Coloring (Shape-from-photoconsistency)mentioning

confidence: 99%

“…The raw imaging data acquired from optical devices such as laser scanners always contains noise [137], which must be taken into account during subsequent post-processing.…”

Section: D Point Set Smoothingmentioning

confidence: 99%

How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques

Harandi

Vandenberghe²,

Vankerschaver

et al. 2023

Plant Methods

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Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.

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“…Metody oparte na sąsiedztwie wykorzystują informacje o punktach bezpośrednio sąsiadujących z danym punktem z chmury punktów. Najbardziej podstawową metodą jest wykorzystanie filtra bilateralnego 3D [29]. Inne metody polegają na przepro-wadzeniu filtrowania w dwóch krokach: w pierwszym następuje usunięcie punktów odstających, a w drugim wygładzenie szumu.…”

Section: Filtrowanie Chmur Punktówunclassified

Point Cloud Filtering Using 2D-3D Matching Method

Rzepka¹,

Kulczykowski²,

Wittels³

2022

PAR

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Precision is a key feature for the development of 3D measurement systems. Time-of-flight cameras used for such measurements create point clouds containing a lot of noise, which may not be useful for further analysis. In our research to solve this problem, we propose a new method for precise point cloud filtering. We use 2D information from a telecentric lens camera to remove outlier points from 3D measurements recorded with a Time-of-Flight camera. The use of a telecentric camera allows us to obtain the most precise information about the contour of an object, which allows us to accurately filter the object reconstruction in 3D.

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Mapping bootstrap error for bilateral smoothing on point set

Cited by 12 publications

References 1 publication

Optimization of 3D Point Clouds of Oilseed Rape Plants Based on Time-of-Flight Cameras

Optimization of 3D Point Clouds of Oilseed Rape Plants Based on Time-of-Flight Cameras

How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques

Point Cloud Filtering Using 2D-3D Matching Method

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