Multi-sensor object detection is an active research topic in automated
driving, but the robustness of such detection models against missing sensor
input (modality missing), e.g., due to a sudden sensor failure, is a critical
problem which remains under-studied. In this work, we propose UniBEV, an
end-to-end multi-modal 3D object detection framework designed for robustness
against missing modalities: UniBEV can operate on LiDAR plus camera input, but
also on LiDAR-only or camera-only input without retraining. To facilitate its
detector head to handle different input combinations, UniBEV aims to create
well-aligned Bird's Eye View (BEV) feature maps from each available modality.
Unlike prior BEV-based multi-modal detection methods, all sensor modalities
follow a uniform approach to resample features from the native sensor
coordinate systems to the BEV features. We furthermore investigate the
robustness of various fusion strategies w.r.t. missing modalities: the commonly
used feature concatenation, but also channel-wise averaging, and a
generalization to weighted averaging termed Channel Normalized Weights. To
validate its effectiveness, we compare UniBEV to state-of-the-art BEVFusion and
MetaBEV on nuScenes over all sensor input combinations. In this setting, UniBEV
achieves $52.5 \%$ mAP on average over all input combinations, significantly
improving over the baselines ($43.5 \%$ mAP on average for BEVFusion, $48.7 \%$
mAP on average for MetaBEV). An ablation study shows the robustness benefits of
fusing by weighted averaging over regular concatenation, and of sharing queries
between the BEV encoders of each modality. Our code will be released upon paper
acceptance.

提出了 UniBEV，一个端到端多模态 3D 物体检测框架，用于解决缺失传感器输入（因突发传感器故障等原因）的鲁棒性问题，通过创建对齐的鸟瞰图特征图处理不同的输入组合，并采用加权平均融合策略提高鲁棒性