VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking

Chen, Yukang; Liu, Jianhui; Zhang, Xiangyu; Qi, Xiaojuan; Jia, Jiaya

doi:10.48550/arxiv.2303.11301

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…It can effectively suppress noise and improve accuracy. Furthermore, VoxelNeXt [21] is a 3D object detection and tracking method that directly predicts objects based on sparse voxel features without relying on hand-crafted proxies. BTCDet [22] predicts the probability of occupancy in these regions.…”

Section: Deep-learning-based Vehicle Pose Estimation Methodsmentioning

confidence: 99%

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Sun

Liu

2023

Electronics

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Vehicle pose estimation with LIDAR plays a crucial role in autonomous driving systems. It serves as the fundamental basis for functions such as tracking, path planning, and decision-making. However, the majority of current vehicle pose estimation techniques struggle to produce satisfactory results when faced with incomplete observation measurements, such as L-shaped point cloud clusters without side contours or those including side-view mirrors. In addition, the requirement for real-time results further increases the difficulty of the pose estimation task. In this paper, we present a vehicle Pose Estimation method with Heuristic L-shape fitting and grid-based Particle Filter (PE-HL-PF). We design a geometric shape classifier module to divide clusters into symmetrical and asymmetrical ones according to their shape features. Furthermore, a contour-based heuristic L-shape fitting module is introduced for asymmetrical clusters, and a structure-aware grid-based particle filter is used to estimate the pose of symmetrical clusters. PE-HL-PF first utilizes a heuristic asymmetrical module that selects dominant contours fitting orientation in a heuristic manner, thereby avoiding the need for a complex traversal search. Additionally, a symmetrical module based on particle filtering is incorporated to enhance the stability of orientation estimation. This method achieves significant improvements in both the runtime efficiency and pose estimation accuracy of incomplete point clouds. Compared with state-of-the-art pose estimation methods, our PE-HL-PF demonstrates a notable performance improvement. Our method can estimate the pose of thousands of objects in less than 1 millisecond, a significant improvement over previous methods. The results of experiments performed on the KITTI dataset validate the effectiveness of our approach.

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Section: Deep-learning-based Vehicle Pose Estimation Methodsmentioning

confidence: 99%

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Sun

Liu

2023

Electronics

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“…CenterPoint [29] presents using point representations to describe objects, which solves the constraints imposed by anchor-based methods on angle and size and diminishes the search space for objects. VoxelNeXt [30] proposes an efficient structure that predicts objects directly from sparse voxel features rather than relying on hand-crafted proxies. The voxel-based methods can achieve decent detection performance with promising efficiency.…”

Section: Lidar-only 3d Object Detection Methodsmentioning

confidence: 99%

SimoSet: A 3D Object Detection Dataset Collected from Vehicle Hybrid Solid-State LiDAR

Sun

Jin

et al. 2023

Electronics

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Three-dimensional (3D) object detection based on point cloud data plays a critical role in the perception system of autonomous driving. However, this task presents a significant challenge in terms of its practical implementation due to the absence of point cloud data from automotive-grade hybrid solid-state LiDAR, as well as the limitations regarding the generalization ability of data-driven deep learning methods. In this paper, we introduce SimoSet, the first vehicle view 3D object detection dataset composed of automotive-grade hybrid solid-state LiDAR data. The dataset was collected from a university campus, contains 52 scenes, each of which are 8 s long, and provides three types of labels for typical traffic participants. We analyze the impact of the installation height and angle of the LiDAR on scanning effect and provide a reference process for the collection, annotation, and format conversion of LiDAR data. Finally, we provide baselines for LiDAR-only 3D object detection.

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VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking

Cited by 2 publications

References 38 publications

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

SimoSet: A 3D Object Detection Dataset Collected from Vehicle Hybrid Solid-State LiDAR

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