Robust and Accurate Feature Detection on Point Clouds

Liu, Zheng; Xin, Xiaozhou; Xu, Zhiqiang; Zhou, Weijie; Wang, Chunxue; Chen, Renjie; He, Ying

doi:10.1016/j.cad.2023.103592

Cited by 6 publications

(2 citation statements)

References 47 publications

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“…In addition, there are also disturbing artifacts caused by reflections and the multipath effect [6]. While classical rule-based approaches have proven to be effective at extracting geometric features such as lines [7,8], the detection of entire objects remains challenging. Consequently, classical rule-based approaches are not suitable for detection tasks.…”

Section: Introductionmentioning

confidence: 99%

Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar

Ruddat,

Reichardt,

Ebert

et al. 2024

Applied Sciences

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Detecting vulnerable road users is a major challenge for autonomous vehicles due to their small size. Various sensor modalities have been investigated, including mono or stereo cameras and 3D LiDAR sensors, which are limited by environmental conditions and hardware costs. Radar sensors are a low-cost and robust option, with high-resolution 4D radar sensors being suitable for advanced detection tasks. However, they involve challenges such as few and irregularly distributed measurement points and disturbing artifacts. Learning-based approaches utilizing pillar-based networks show potential in overcoming these challenges. However, the severe sparsity of radar data makes detecting small objects with only a few points difficult. We extend a pillar network with our novel Sparsity-Robust Feature Fusion (SRFF) neck, which combines high- and low-level multi-resolution features through a lightweight attention mechanism. While low-level features aid in better localization, high-level features allow for better classification. As sparse input data are propagated through a network, the increasing effective receptive field leads to feature maps of different sparsities. The combination of features with different sparsities improves the robustness of the network for classes with few points.

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Section: Introductionmentioning

confidence: 99%

Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar

Ruddat,

Reichardt,

Ebert

et al. 2024

Applied Sciences

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“…The extraction of geometric boundaries of objects or parts in a point cloud, such as line segments [40] and contours [41], is a fundamental problem in point cloud processing. This allows for more accurate segmentation and surface reconstruction [42]. The 3D alpha shape was first applied for point cloud filtering by Ma et al by combining the ball pivot algorithm (BPA) and spatial sorting [43].…”

Section: Introductionmentioning

confidence: 99%

A Multiscale Filtering Method for Airborne LiDAR Data Using Modified 3D Alpha Shape

Cao,

Wang,

et al. 2024

Remote Sensing

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The complexity of terrain features poses a substantial challenge in the effective processing and application of airborne LiDAR data, particularly in regions characterized by steep slopes and diverse objects. In this paper, we propose a novel multiscale filtering method utilizing a modified 3D alpha shape algorithm to increase the ground point extraction accuracy in complex terrain. Our methodology comprises three pivotal stages: preprocessing for outlier removal and potential ground point extraction; the deployment of a modified 3D alpha shape to construct multiscale point cloud layers; and the use of a multiscale triangulated irregular network (TIN) densification process for precise ground point extraction. In each layer, the threshold is adaptively determined based on the corresponding α. Points closer to the TIN surface than the threshold are identified as ground points. The performance of the proposed method was validated using a classical benchmark dataset provided by the ISPRS and an ultra-large-scale ground filtering dataset called OpenGF. The experimental results demonstrate that this method is effective, with an average total error and a kappa coefficient on the ISPRS dataset of 3.27% and 88.97%, respectively. When tested in the large scenarios of the OpenGF dataset, the proposed method outperformed four classical filtering methods and achieved accuracy comparable to that of the best of learning-based methods.

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