Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds

Object detection in 3D point clouds is still a challenging task in autonomous driving. Due to the inherent occlusion and density changes of the point cloud, the data distribution of the same object will change dramatically. Especially, the incomplete data with sparsity or occlusion can not represent the complete characteristics of the object. In this paper, we proposed a novel strong–weak feature alignment algorithm between complete and incomplete objects for 3D object detection, which explores the correlations within the data. It is an end-to-end adaptive network that does not require additional data and can be easily applied to other object detection networks. Through a complete object feature extractor, we achieve a robust feature representation of the object. It serves as a guarding feature to help the incomplete object feature generator to generate effective features. The strong–weak feature alignment algorithm reduces the gap between different states of the same object and enhances the ability to represent the incomplete object. The proposed adaptation framework is validated on the KITTI object benchmark and gets about 6% improvement in detection average precision on 3D moderate difficulty compared to the basic model. The results show that our adaptation method improves the detection performance of incomplete 3D objects.

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Out-of-core real-time visualization of massive 3D point clouds

Proceedings of the 7th International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa - AFRIGRAPH '10 ◽

10.1145/1811158.1811178 ◽

2010 ◽

Cited By ~ 18

Author(s):

Rico Richter ◽

Jürgen Döllner

Keyword(s):

Real Time ◽

Point Clouds ◽

3D Point Clouds ◽

Real Time Visualization

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Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096514666211026142721 ◽

2021 ◽

Vol 14 ◽

Author(s):

Zhiyong Gao ◽

Jianhong Xiang

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Real Time ◽

Point Cloud ◽

Point Clouds ◽

3D Point Cloud ◽

3D Object ◽

3D Object Detection ◽

Instance Segmentation

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

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