One-Stage Anchor-Free 3D Vehicle Detection from LiDAR Sensors

Li, Hao; Zhao, Shirong; Zhao, Wenjun; Zhang, Libin; Shen, Jianbing

doi:10.3390/s21082651

Cited by 18 publications

(7 citation statements)

References 34 publications

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“…A graph neural network [37, 38] was proposed to enhance the detection precision by using spatiotemporal transformer attention. Another one-stage method [39] was proposed for 3D vehicle detection which applied an anchor-free head to improve the calculation efficiency. As the ground truth labels were significant for model training, the weakly supervised framework [40, 41] was studied to train a neural network model using few samples.…”

Section: Related Workmentioning

confidence: 99%

PLOT: a 3D point cloud object detection network for autonomous driving

Zhang¹,

Wang²,

Dai³

2023

Robotica

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3D object detection using point cloud is an essential task for autonomous driving. With the development of infrastructures, roadside perception can extend the view range of the autonomous vehicles through communication technology. Computation time and power consumption are two main concerns when deploying object detection tasks, and a light-weighted detection model applied in an embedded system is a convenient solution for both roadside and vehicleside. In this study, a 3D Point cLoud Object deTection (PLOT) network is proposed to reduce heavy computing and ensure real-time object detection performance in an embedded system. First, a bird’s eye view representation of the point cloud is calculated using pillar-based encoding method. Then a cross-stage partial network-based backbone and a feature pyramid network-based neck are implemented to generate the high-dimensional feature map. Finally, a multioutput head using a shared convolutional layer is attached to predict classes, bounding boxes, and the orientations of the objects at the same time. Extensive experiments using the Waymo Open Dataset and our own dataset are conducted to demonstrate the accuracy and efficiency of the proposed method.

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Section: Related Workmentioning

confidence: 99%

PLOT: a 3D point cloud object detection network for autonomous driving

Zhang¹,

Wang²,

Dai³

2023

Robotica

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“…Other methods based on neural networks, for 3-D object detection, were presented in [ 23 , 24 , 25 , 26 , 27 , 28 ]. In these approaches, single-stage or more complex (two-stage pyramidal, in [ 24 ]) networks are proposed and evaluated on the KITTI dataset.…”

Section: Related Workmentioning

confidence: 99%

Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

Dulău

Oniga

2021

Sensors

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In this paper, we propose an obstacle detection approach that uses a facet-based obstacle representation. The approach has three main steps: ground point detection, clustering of obstacle points, and facet extraction. Measurements from a 64-layer LiDAR are used as input. First, ground points are detected and eliminated in order to select obstacle points and create object instances. To determine the objects, obstacle points are grouped using a channel-based clustering approach. For each object instance, its contour is extracted and, using an RANSAC-based approach, the obstacle facets are selected. For each processing stage, optimizations are proposed in order to obtain a better runtime. For the evaluation, we compare our proposed approach with an existing approach, using the KITTI benchmark dataset. The proposed approach has similar or better results for some obstacle categories but a lower computational complexity.

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“…Therefore, LiDAR sensors can function well even in adverse illumination conditions. With robust depth measurements, LiDAR sensors are crucial to many applications such as autonomous vehicles [ 2 , 3 ], classification [ 4 ], and instance detection [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

LiDAR Intensity Completion: Fully Exploiting the Message from LiDAR Sensors

Dai

Chen

Huang

et al. 2022

Sensors

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Light Detection and Ranging (LiDAR) systems are novel sensors that provide robust distance and reflection strength by active pulsed laser beams. They have significant advantages over visual cameras by providing active depth and intensity measurements that are robust to ambient illumination. However, the systemsstill pay limited attention to intensity measurements since the output intensity maps of LiDAR sensors are different from conventional cameras and are too sparse. In this work, we propose exploiting the information from both intensity and depth measurements simultaneously to complete the LiDAR intensity maps. With the completed intensity maps, mature computer vision techniques can work well on the LiDAR data without any specific adjustment. We propose an end-to-end convolutional neural network named LiDAR-Net to jointly complete the sparse intensity and depth measurements by exploiting their correlations. For network training, an intensity fusion method is proposed to generate the ground truth. Experiment results indicate that intensity–depth fusion can benefit the task and improve performance. We further apply an off-the-shelf object (lane) segmentation algorithm to the completed intensity maps, which delivers consistent robust to ambient illumination performance. We believe that the intensity completion method allows LiDAR sensors to cope with a broader range of practice applications.

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One-Stage Anchor-Free 3D Vehicle Detection from LiDAR Sensors

Cited by 18 publications

References 34 publications

PLOT: a 3D point cloud object detection network for autonomous driving

PLOT: a 3D point cloud object detection network for autonomous driving

Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

LiDAR Intensity Completion: Fully Exploiting the Message from LiDAR Sensors

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