Affiliation:
1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Abstract
In order to study the modern 3D object detection algorithm based on deep learning, this paper studies the point-based 3D object detection algorithm, that is, a 3D object detection algorithm that uses multilayer perceptron to extract point features. This paper proposes a method based on point RCNN. A three-stage 3D object detection algorithm improves the accuracy of the algorithm by fusing image information. The algorithm in this paper integrates the information and image information of the three stages well, which improves the information utilization of the whole algorithm. Compared with the traditional 3D target detection algorithm, the structure of the algorithm in this paper is more compact, which effectively improves the utilization of information.
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Reference25 articles.
1. ImageNet classification with deep convolutional neural networks;A. Krizhevsky;Advances in Neural Information Processing Systems,2012
2. Faster rcnn: towards real-time object detection with region proposal networks;S. Ren;Advances in Neural Information Processing Systems,2015
3. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
4. Augmented reality technologies, systems and applications
5. Microsoft Kinect Sensor and Its Effect
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