Bridged Transformer for Vision and Point Cloud 3D Object Detection
This addresses a key bottleneck in multimodal 3D object detection for applications like autonomous driving and robotics, though it is incremental as it builds on existing transformer-based fusion methods.
The paper tackles the problem of fusing 2D images and 3D point clouds for 3D object detection by proposing Bridged Transformer (BrT), which uses object queries to unify these representations and achieves state-of-the-art results on SUN RGB-D and ScanNetV2 datasets.
3D object detection is a crucial research topic in computer vision, which usually uses 3D point clouds as input in conventional setups. Recently, there is a trend of leveraging multiple sources of input data, such as complementing the 3D point cloud with 2D images that often have richer color and fewer noises. However, due to the heterogeneous geometrics of the 2D and 3D representations, it prevents us from applying off-the-shelf neural networks to achieve multimodal fusion. To that end, we propose Bridged Transformer (BrT), an end-to-end architecture for 3D object detection. BrT is simple and effective, which learns to identify 3D and 2D object bounding boxes from both points and image patches. A key element of BrT lies in the utilization of object queries for bridging 3D and 2D spaces, which unifies different sources of data representations in Transformer. We adopt a form of feature aggregation realized by point-to-patch projections which further strengthen the correlations between images and points. Moreover, BrT works seamlessly for fusing the point cloud with multi-view images. We experimentally show that BrT surpasses state-of-the-art methods on SUN RGB-D and ScanNetV2 datasets.