Abstract: Due to the highly sparsity of point cloud data,current 3D object detection methods based on point cloud are inadequate for learning local features,and some invalid information contained in point cloud data can interfere with object detection.To address the above problems,a 3D object detection model based on edge convolution(EdgeConv) and bottleneck attention module(BAM) is proposed.First,by creating a K-nearest-neighbor graph structure for each point in point clouds on the feature space,multilayer edge convolutions are constructed to learn the multi-scale local features of point clouds.Second,a bottleneck attention module(BAM) is designed for 3D point cloud data,and each BAM consists of a channel attention module and a spatial attention module to enhance the point cloud information that is valuable for object detection,aiming to strengthen the feature representation of the proposed model.The network uses VoteNet as the baseline,and multilayer edge convolutions and BAM are added sequentially between PointNet++ and the voting module.The proposed model is evaluated and compared with other 13 state-of-the-art methods on two benchmark datasets SUN RGB-D and ScanNetV2.Experimental results demonstrate that on SUN RGB-D dataset,the proposed model achieves the highest mAP@0.5,and the highest AP@0.25 for six out of ten categories such as bed,chair and desk.On ScanNetV2 dataset,this model outperforms other 13 methods in terms of mAP under both IoU 0.25 and 0.5,and achieves the highest AP@0.25 for ten out of eighteen categories such as chair,sofa and picture.As compared to the baseline VoteNet,the mAP@0.25 of the proposed model improves by 6.5% and 12.9% respectively on two datasets.Ablation studies are conducted to verify the contributions of each component.

Key words: 3D object detection, Point clouds, Edge convolution, Bottleneck attention module, VoteNet, SUN RGB-D dataset, ScanNetV2 dataset