Journal Article10.1016/j.aei.2023.101971
An object detection algorithm combining semantic and geometric information of the 3D point cloud
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TL;DR: Zhang et al. as mentioned in this paper proposed two modules for enhancing useful feature extraction in the SA layer to improve 3D object detection accuracy, focusing on the foreground and boundary scores of the points and reweighing the Furthest Point Sampling (FPS) using the evaluated scores.
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About: This article is published in Advanced Engineering Informatics. The article was published on 01 Apr 2023. The article focuses on the topics: Computer science & Point cloud.
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VSL-Net: Voxel structure learning for 3D object detection
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TL;DR: VSL-Net proposes a single-stage 3D object detection method that integrates voxel structure learning, submanifold sparse convolution, and position alignment for accurate foreground point segmentation, center estimation, and vehicle detection with 92.12% accuracy on Kitti, Waymo, and Nuscene datasets.
3
Prediction method of impact deformation mode based on multimodal fusion with point cloud sequences: Applied to thin-walled structures
Chen-Han Yang,Zhaoyang Li,Ping Xu,Huichao Huang,Yujia Huo,Yuyang Wei +5 more
TL;DR: A multimodal fusion prediction method is proposed for impact deformation mode of thin-walled structures, utilizing point cloud sequences and structural design variables, achieving 5% relative error and 8169x speedup over finite element simulation.
2
Pillarnext: Improving the Point Cloud Based 3d Object Detection for Autonomous Driving by Constructing Multi-Scale Features
Xusheng Li,Chengliang Wang,Shumao Wang,Zhuo Zhang,Ji Liu,Bo Zheng +5 more
- 01 Jan 2024
References
Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger,Philip Lenz,Raquel Urtasun +2 more
- 16 Jun 2012
TL;DR: The autonomous driving platform is used to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection, revealing that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
R. Qi Charles,Hao Su,Mo Kaichun,Leonidas J. Guibas +3 more
- 21 Jul 2017
TL;DR: This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.
•Posted Content
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
TL;DR: A hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set and proposes novel set learning layers to adaptively combine features from multiple scales to learn deep point set features efficiently and robustly.
Dynamic Graph CNN for Learning on Point Clouds
TL;DR: This work proposes a new neural network module suitable for CNN-based high-level tasks on point clouds, including classification and segmentation called EdgeConv, which acts on graphs dynamically computed in each layer of the network.
•Proceedings Article
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Charles R. Qi,Li Yi,Hao Su,Leonidas J. Guibas +3 more
- 07 Jun 2017
TL;DR: PointNet++ as discussed by the authors applies PointNet recursively on a nested partitioning of the input point set to learn local features with increasing contextual scales, and proposes novel set learning layers to adaptively combine features from multiple scales.
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