三维目标检测的同质多模态特征融合与交互

Xin Li, Botian Shi, Yuenan Hou, Xingjiao Wu, Tianlong Ma, Yikang Li, Liangbo He
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引用次数: 19

摘要

多模态三维目标检测一直是自动驾驶领域的研究热点。然而,探索稀疏的3D点与密集的2D像素之间的跨模态特征融合并非易事。最近的方法要么将图像特征与投影到二维图像平面上的点云特征融合,要么将稀疏的点云与密集的图像像素结合起来。这些融合方法经常遭受严重的信息丢失,从而导致次优性能。为了解决这些问题,我们在点云和图像之间构建同质结构,通过将相机特征转换到LiDAR三维空间来避免投影信息的丢失。本文提出了一种均匀多模态特征融合与交互方法(HMFI)用于三维目标检测。具体来说,我们首先设计了一个图像体素提升模块(IVLM),将二维图像特征提升到三维空间,并生成均匀的图像体素特征。然后,通过引入基于自关注的查询融合机制(QFM),将体素化的点云特征与来自不同区域的图像特征进行融合;接下来,我们提出了一个体素特征交互模块(VFIM)来增强同质点云和图像体素表示中相同对象语义信息的一致性,可以为跨模态特征融合提供对象级对齐指导,增强复杂背景下的判别能力。我们在KITTI和Waymo开放数据集上进行了大量的实验,与最先进的多模态方法相比,所提出的HMFI取得了更好的性能。特别是在KITTI基准上对自行车手的三维检测,HMFI大大超过了所有已发表的算法。
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Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection
Multi-modal 3D object detection has been an active research topic in autonomous driving. Nevertheless, it is non-trivial to explore the cross-modal feature fusion between sparse 3D points and dense 2D pixels. Recent approaches either fuse the image features with the point cloud features that are projected onto the 2D image plane or combine the sparse point cloud with dense image pixels. These fusion approaches often suffer from severe information loss, thus causing sub-optimal performance. To address these problems, we construct the homogeneous structure between the point cloud and images to avoid projective information loss by transforming the camera features into the LiDAR 3D space. In this paper, we propose a homogeneous multi-modal feature fusion and interaction method (HMFI) for 3D object detection. Specifically, we first design an image voxel lifter module (IVLM) to lift 2D image features into the 3D space and generate homogeneous image voxel features. Then, we fuse the voxelized point cloud features with the image features from different regions by introducing the self-attention based query fusion mechanism (QFM). Next, we propose a voxel feature interaction module (VFIM) to enforce the consistency of semantic information from identical objects in the homogeneous point cloud and image voxel representations, which can provide object-level alignment guidance for cross-modal feature fusion and strengthen the discriminative ability in complex backgrounds. We conduct extensive experiments on the KITTI and Waymo Open Dataset, and the proposed HMFI achieves better performance compared with the state-of-the-art multi-modal methods. Particularly, for the 3D detection of cyclist on the KITTI benchmark, HMFI surpasses all the published algorithms by a large margin.
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