Robust 3D keypoint detection method based on double Gaussian weighted dissimilarity measure

Zeng, Hui; Wang, Huijuan; Dong, Jie

doi:10.1007/s11042-016-4139-y

Cited by 6 publications

(5 citation statements)

References 16 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhao et al 19 implemented a geometric segmentation algorithm to distinguish between target and ground areas in LiDAR data, then deeply classified corresponding images captured by the camera using a fuzzy logic inference framework to integrate LiDAR data with imagery for frame-by-frame analysis. Zeng et al 20 identified candidate keypoints with high local heteroskedasticity values by computing shape indices and dual Gaussian weighted metrics for each 3D point, facilitating 3D model identification and alignment. This approach was effectively employed by numerous teams during the 2007 DARPA Urban Challenge to segment point clouds and detect vehicles on the track.…”

Section: Related Workmentioning

confidence: 99%

Neural network-based 3D point cloud detection of targets in unstructured environments

Wang,

Yang,

Yao

et al. 2024

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Accurate environmental sensing is an important prerequisite for autonomous driving in off-road environments. Most targets in off-road environments do not have regular shapes, colors, textures and other features, making them difficult to identify. In addition, complex driving conditions can cause large, broadband vibrations in off-road vehicles, which interfere with environment sensing and affect the accuracy and efficiency of perception. To address the above problems, this paper proposes an improved 3D point cloud filtering algorithm for unstructured environments and a point cloud classification method using neural networks, and provides an experimental proof-of-principle of the proposed methods. A comparison of the results under six conditions shows that the amount of data processed by the improved filtering algorithm is 65%–85% of that processed by the conventional filtering algorithm, and the trained neural network model achieves an accuracy of 98.0% and a loss value as low as 0.008 when classifying three typical targets in an unstructured environment. A comparison with algorithms proposed in other papers shows that the proposed method is highly feasible.

show abstract

Section: Related Workmentioning

confidence: 99%

Neural network-based 3D point cloud detection of targets in unstructured environments

Wang,

Yang,

Yao

et al. 2024

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…Despite achieving an even distribution of keypoints, LSP's repeatability is relatively poor. Building upon LSP, Zeng et al [14] presented the double Gaussian weighted dissimilarity measure (DGWDM) keypoint detection algorithm. DGWDM detects keypoints in two stages.…”

Section: Related Workmentioning

confidence: 99%

Point Cloud Registration Based on Local Variation of Surface Keypoints

Zhu,

Huang,

Yue

et al. 2023

Electronics

View full text Add to dashboard Cite

Keypoint detection plays a pivotal role in three-dimensional computer vision, with widespread applications in improving registration precision and efficiency. However, current keypoint detection methods often suffer from poor robustness and low discriminability. In this study, a novel keypoint detection approach based on the local variation of surface (LVS) is proposed. The LVS keypoint detection method comprises three main steps. Firstly, the surface variation index for each point is calculated using the local coordinate system. Subsequently, points with a surface variation index lower than the local average are identified as initial keypoints. Lastly, the final keypoints are determined by selecting the minimum value within the neighborhood from the initial keypoints. Additionally, a sampling consensus correspondence estimation algorithm based on geometric constraints (SAC-GC) for efficient and robust estimation of optimal transformations in correspondences is proposed. By combining LVS and SAC-GC, we propose a coarse-to-fine point cloud registration algorithm. Experimental results on four public datasets demonstrate that the LVS keypoint detection algorithm offers improved repeatability and robustness, particularly when dealing with noisy, occluded, or cluttered point clouds. The proposed coarse-to-fine point cloud registration algorithm also exhibits enhanced robustness and computational efficiency.

show abstract

“…This enables them to extract the objects' features so as to divide the objects into different categories. Zeng et al [28] proposed a kind of novel multiscale 3D keypoint detection method by using the double Gaussian weighted dissimilarity measure. The shape index value and the Gaussian weighted value of each 3D point were computed to select the most suitable 3D multi-scale key points.…”

Section: Related Workmentioning

confidence: 99%

Classifying 3D objects in LiDAR point clouds with a back-propagation neural network

Song

Zou

Tian³

et al. 2018

Hum. Cent. Comput. Inf. Sci.

View full text Add to dashboard Cite

Autonomous driving technologies enable motor vehicles to drive themselves safely and reliably, and are being widely researched for smart cities and urban services [1]. The ability to perceive their surroundings is essential for unmanned ground vehicles (UGVs) to achieve autonomous driving [2]. Autonomous UGVs need to obtain a large amount of accurate environmental data to support automatic object avoidance and local path planning [3]. Several types of environment sensors, such as fisheye, binocular, and depth cameras, are widely used to obtain real-time information about a vehicle's surroundings so it can be aware of its environment [4-6]. Compared with these, the clearest advantage of Light Detection and Ranging (LiDAR) is that it can rapidly collect high-precision, wide-range point clouds [7]. Classifying and recognizing the features of individual objects based on these point clouds is a crucial challenge, and involves exploiting their unique properties,

show abstract

Robust 3D keypoint detection method based on double Gaussian weighted dissimilarity measure

Cited by 6 publications

References 16 publications

Neural network-based 3D point cloud detection of targets in unstructured environments

Neural network-based 3D point cloud detection of targets in unstructured environments

Point Cloud Registration Based on Local Variation of Surface Keypoints

Classifying 3D objects in LiDAR point clouds with a back-propagation neural network

Contact Info

Product

Resources

About