Cascaded Cross-Modality Fusion Network for 3D Object Detection

Clicks: 270
ID: 266218
2020
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Abstract
We focus on exploring the LIDAR-RGB fusion-based 3D object detection in this paper. This task is still challenging in two aspects: (1) the difference of data formats and sensor positions contributes to the misalignment of reasoning between the semantic features of images and the geometric features of point clouds. (2) The optimization of traditional IoU is not equal to the regression loss of bounding boxes, resulting in biased back-propagation for non-overlapping cases. In this work, we propose a cascaded cross-modality fusion network (CCFNet), which includes a cascaded multi-scale fusion module (CMF) and a novel center 3D IoU loss to resolve these two issues. Our CMF module is developed to reinforce the discriminative representation of objects by reasoning the relation of corresponding LIDAR geometric capability and RGB semantic capability of the object from two modalities. Specifically, CMF is added in a cascaded way between the RGB and LIDAR streams, which selects salient points and transmits multi-scale point cloud features to each stage of RGB streams. Moreover, our center 3D IoU loss incorporates the distance between anchor centers to avoid the oversimple optimization for non-overlapping bounding boxes. Extensive experiments on the KITTI benchmark have demonstrated that our proposed approach performs better than the compared methods.
Reference Key
chen2020sensorscascaded Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhiyu Chen;Qiong Lin;Jing Sun;Yujian Feng;Shangdong Liu;Qiang Liu;Yimu Ji;He Xu;Chen, Zhiyu;Lin, Qiong;Sun, Jing;Feng, Yujian;Liu, Shangdong;Liu, Qiang;Ji, Yimu;Xu, He;
Journal sensors
Year 2020
DOI
10.3390/s20247243
URL
https://www.mdpi.com/1424-8220/20/24/7243/htm
Keywords
lidar multi-sensor fusion 3d object detection point cloud processing

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