基于RGB-D视频流的室内场景三维目标高效检测

Q3 Computer Science

计算机辅助设计与图形学学报 Pub Date : 2021-07-01 DOI:10.3724/sp.j.1089.2021.18630

Miao Yongwei, Jiahui Chen, Xinjie Zhang, Ma Wenjuan, S. Sun

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引用次数: 1

摘要

:对于室内目标检测，输入的复杂场景往往存在RGB-D扫描数据不完整或其目标相互遮挡等缺陷。同时，由于RGB-D视频流中帧数的限制。利用SUN RGB-D数据集对关键帧的目标检测网络进行训练，检测结果准确，与基于VoteNet的逐帧检测方案相比，整体检测时间大大缩短。实验结果表明，该方法是有效的。

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Efficient 3D Object Detection of Indoor Scenes Based on RGB-D Video Stream

: For indoor object detection, the input complex scenes often have some defects such as incomplete RGB-D scanning data or mutual occlusion of its objects. Meanwhile, due to the limitations of frame in the RGB-D video stream. Using SUN RGB-D dataset to train the object detection network of key frame, the detection result of proposed method is accurate, and the overall detection time is greatly reduced if com-paring with the VoteNet based frame-by-frame detection scheme. Experimental results demonstrate that proposed method is effective and efficient.

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来源期刊

计算机辅助设计与图形学学报 Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

1.20

自引率

0.00%

发文量

6833

期刊介绍：