Multi-label Point Cloud Annotation by Selection of Sparse Control Points

Monica, Riccardo; Aleotti, Jacopo; Zillich, Michael; Vincze, Markus

doi:10.1109/3dv.2017.00042

Cited by 23 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each sequence, five images were chosen, and based on these, the point clouds were generated. Then, reference point clouds were prepared using the RViz Cloud Annotation Tool [ 47 ]. Finally, reference maps were derived from the annotated point clouds.…”

Section: Experiments Overview and Resultsmentioning

confidence: 99%

Obstacle Detection System for Agricultural Mobile Robot Application Using RGB-D Cameras

Skoczeń

Ochman

Spyra³

et al. 2021

Sensors

View full text Add to dashboard Cite

Mobile robots designed for agricultural tasks need to deal with challenging outdoor unstructured environments that usually have dynamic and static obstacles. This assumption significantly limits the number of mapping, path planning, and navigation algorithms to be used in this application. As a representative case, the autonomous lawn mowing robot considered in this work is required to determine the working area and to detect obstacles simultaneously, which is a key feature for its working efficiency and safety. In this context, RGB-D cameras are the optimal solution, providing a scene image including depth data with a compromise between precision and sensor cost. For this reason, the obstacle detection effectiveness and precision depend significantly on the sensors used, and the information processing approach has an impact on the avoidance performance. The study presented in this work aims to determine the obstacle mapping accuracy considering both hardware- and information processing-related uncertainties. The proposed evaluation is based on artificial and real data to compute the accuracy-related performance metrics. The results show that the proposed image and depth data processing pipeline introduces an additional distortion of 38 cm.

show abstract

Section: Experiments Overview and Resultsmentioning

confidence: 99%

Obstacle Detection System for Agricultural Mobile Robot Application Using RGB-D Cameras

Skoczeń

Ochman

Spyra³

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Two-dimensional image annotation relies on many existing tools, such as labelme [ 21 ]. There are also related tools for 3D point cloud annotation, such as the shortest path tree-based segmentation algorithm developed by Monica et al [ 22 ]. Yan et al [ 23 ] developed a semiautomatic point cloud annotation tool based on point cloud clustering.…”

Section: Methodsmentioning

confidence: 99%

Panicle-3D: Efficient Phenotyping Tool for Precise Semantic Segmentation of Rice Panicle Point Cloud

Gong

Zhu

et al. 2021

Plant Phenomics

View full text Add to dashboard Cite

The automated measurement of crop phenotypic parameters is of great significance to the quantitative study of crop growth. The segmentation and classification of crop point cloud help to realize the automation of crop phenotypic parameter measurement. At present, crop spike-shaped point cloud segmentation has problems such as fewer samples, uneven distribution of point clouds, occlusion of stem and spike, disorderly arrangement of point clouds, and lack of targeted network models. The traditional clustering method can realize the segmentation of the plant organ point cloud with relatively independent spatial location, but the accuracy is not acceptable. This paper first builds a desktop-level point cloud scanning apparatus based on a structured-light projection module to facilitate the point cloud acquisition process. Then, the rice ear point cloud was collected, and the rice ear point cloud data set was made. In addition, data argumentation is used to improve sample utilization efficiency and training accuracy. Finally, a 3D point cloud convolutional neural network model called Panicle-3D was designed to achieve better segmentation accuracy. Specifically, the design of Panicle-3D is aimed at the multiscale characteristics of plant organs, combined with the structure of PointConv and long and short jumps, which accelerates the convergence speed of the network and reduces the loss of features in the process of point cloud downsampling. After comparison experiments, the segmentation accuracy of Panicle-3D reaches 93.4%, which is higher than PointNet. Panicle-3D is suitable for other similar crop point cloud segmentation tasks.

show abstract

“…The test results were compared in terms of segmentation accuracy and the time elapsed for segmentation. In order to calculate the segmentation accuracy, point-wise labeling was performed for each point cloud data through the RViz cloud annotation tool (Monica et al, 2017). Then, we determined paired segments between the segmentation result of a method and the labeled data.…”

Section: Methodsmentioning

confidence: 99%

“…To achieve this, the experiments were conducted with Fukuoka indoor laser dataset containing point cloud data, which has different noise levels (Martinez Mozos et al, 2019). After some preprocessing operations were applied to the dataset, point-wise labeling was performed with the RViz cloud annotation tool (Monica et al, 2017). The test results were compared in terms of segmentation accuracy and the time elapsed for segmentation.…”

Section: Bsjournalsmentioning

confidence: 99%

A Comparative Study for Indoor Planar Surface Segmentation via 3D Laser Point Cloud Data

Eruyar

Yilmaz

et al. 2020

Black Sea Journal of Engineering and Science

View full text Add to dashboard Cite

In recent years, point cloud data generated with RGB-D cameras, 3D lasers, and 3D LiDARs have been employed frequently in robotic applications. In indoor environments, RGB-D cameras, which have short-range and can only describe the vicinity of the robots, generally are opted due to their low cost. On the other hand, 3D lasers and LiDARs can capture long-range measurements and generally are used in outdoor applications. In this study, we deal with the segmentation of indoor planar surfaces such as wall, floor, and ceiling via point cloud data. The segmentation methods, which are situated in Point Cloud Library (PCL) were executed with 3D laser point cloud data. The experiments were conducted to evaluate the performance of these methods with the publicly available Fukuoka indoor laser dataset, which has point clouds with different noise levels. The test results were compared in terms of segmentation accuracy and the time elapsed for segmentation. Besides, the general characteristics of each method were discussed. In this way, we revealed the positive and negative aspects of these methods for researchers that plan to apply them to 3D laser point cloud data.

show abstract

Multi-label Point Cloud Annotation by Selection of Sparse Control Points

Cited by 23 publications

References 15 publications

Obstacle Detection System for Agricultural Mobile Robot Application Using RGB-D Cameras

Obstacle Detection System for Agricultural Mobile Robot Application Using RGB-D Cameras

Panicle-3D: Efficient Phenotyping Tool for Precise Semantic Segmentation of Rice Panicle Point Cloud

A Comparative Study for Indoor Planar Surface Segmentation via 3D Laser Point Cloud Data

Contact Info

Product

Resources

About