USING EDGECONV TO IMPROVE 3D OBJECT DETECTION FROM RGB-D DATA
- 1Fujian Key Laboratory of Sensing and Computing, School of Informatics, Xiamen University, 422 Siming Road South, Xiamen 361005, China
- 2Mobile Mapping Lab, Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Keywords: 3D Object Detection, Point Clouds, RGB-D, Frustum, Graph Convolution, Deep Learning
Abstract. In this paper, we proposed a novel 3D deep learning model for object localization and object bounding boxes estimation. To increase the detection efficiency of small objects in the large scale scenes, the local neighbourhood geometric structure information of objects has been taken into the Edgeconv model, which can operate the original point clouds. We evaluated the 3D bounding box with high resolution in the RGB-D dataset and acquired stable effectiveness even under the sparse points and the strong occlusion. The experimental results indicate that our method achieved the higher mean average precision and better IOU of bounding boxes in SUN RGB-D dataset and KITTI benchmark.