3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection

• URL: http://arxiv.org/abs/2211.13529v1
• Date: Thu, 24 Nov 2022 11:00:50 GMT
• Title: 3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection
• Authors: Yecheol Kim, Konyul Park, Minwook Kim, Dongsuk Kum, Jun Won Choi
• Abstract summary: We propose a novel camera-LiDAR fusion architecture called 3D Dual-Fusion. The proposed method fuses the features of the camera-view and 3D voxel-view domain and models their interactions through deformable attention. The results of an experimental evaluation show that the proposed camera-LiDAR fusion architecture achieved competitive performance on the KITTI and nuScenes datasets.
• Score: 13.068266058374775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust 3D object detection. One key challenge in camera-LiDAR fusion involves mitigating the large domain gap between the two sensors in terms of coordinates and data distribution when fusing their features. In this paper, we propose a novel camera-LiDAR fusion architecture called, 3D Dual-Fusion, which is designed to mitigate the gap between the feature representations of camera and LiDAR data. The proposed method fuses the features of the camera-view and 3D voxel-view domain and models their interactions through deformable attention. We redesign the transformer fusion encoder to aggregate the information from the two domains. Two major changes include 1) dual query-based deformable attention to fuse the dual-domain features interactively and 2) 3D local self-attention to encode the voxel-domain queries prior to dual-query decoding. The results of an experimental evaluation show that the proposed camera-LiDAR fusion architecture achieved competitive performance on the KITTI and nuScenes datasets, with state-of-the-art performances in some 3D object detection benchmarks categories.

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