3D object detection models that exploit both LiDAR and camera sensor features are top performers in large-scale autonomous driving benchmarks. A transformer is a popular network architecture used for this task, in which so-called object queries act as candidate objects. Initializing these object queries based on current sensor inputs is a common practice. For this, existing methods strongly rely on LiDAR data however, and do not fully exploit image features. Besides, they introduce significant latency. To overcome these limitations we propose EfficientQ3M, an efficient, modular, and multimodal solution for object query initialization for transformer-based 3D object detection models. The proposed initialization method is combined with a "modality-balanced" transformer decoder where the queries can access all sensor modalities throughout the decoder. In experiments, we outperform the state of the art in transformer-based LiDAR object detection on the competitive nuScenes benchmark and showcase the benefits of input-dependent multimodal query initialization, while being more efficient than the available alternatives for LiDAR-camera initialization. The proposed method can be applied with any combination of sensor modalities as input, demonstrating its modularity.

翻译：利用激光雷达和相机传感器特征的3D目标检测模型在大规模自动驾驶基准测试中表现卓越。Transformer作为该任务的常用网络架构，其所谓的“目标查询”充当候选对象。基于当前传感器输入初始化这些目标查询是常见做法。然而，现有方法严重依赖激光雷达数据，未能充分利用图像特征，同时引入了显著延迟。为克服这些局限性，我们提出EfficientQ3M——一种用于基于Transformer的3D目标检测模型的高效、模块化、多模态目标查询初始化方案。该初始化方法与“模态平衡”Transformer解码器结合，使得查询在整个解码过程中可访问所有传感器模态。实验表明，我们在竞争激烈的nuScenes基准测试中超越了基于Transformer的激光雷达目标检测最先进方法，并证明了输入依赖的多模态查询初始化的优势，同时比现有激光雷达-相机初始化替代方案更高效。所提方法可适用于任意传感器模态组合作为输入，展示了其模块化特性。