DA-Net: Density-Aware 3D Object Detection Network for Point Clouds

Abstract

3D object detection is an important but demanding task, which has become an active research topic in the field of multimedia. Much recent research has been devoted to exploiting end-to-end trainable object detection networks with point clouds. However, most state-of-the-art methods have bottlenecks in detecting occluded objects and small objects, because the sparseness of point clouds is exacerbated on these objects. In this paper, a Density-Aware 3D object detection network (DA-Net) is proposed to improve the perception performance for detecting occluded and small objects, which contains four components: a backbone module with an inverse density scoring module (IDM) and a point-wise attention module (PAM), a 3D intersection over union Estimation Module (3DEM), a Consistent Label Assignment (CLA) method and an Adaptive-Soft-NMS method. The proposed backbone module makes the network concentrate on low-density points of occluded objects, and suppresses outliers and background points. Then, the 3DEM is introduced to evaluate the localization quality of the prediction boxes. Furthermore, the proposed CLA method can more accurately select positive and negative samples for small objects. Finally, Adaptive-Soft-NMS is proposed in our method to reduce the number of false detections during inference and thereby improve detection performance substantially. Extensive experiments demonstrated that the proposed method achieves state-of-the-art performance on two large-scale datasets, SUN RGB-D (62.1% in terms of mAP@0.25) and ScanNetV2 (67.1% in terms of mAP@0.25), and in particular, the detection accuracy of small objects and occluded objects are extremely improved.