Robustness experiments against LiDAR Malfunctions
To validate the robustness of our framework, we evaluate detectors under two LiDAR malfunctions: i) when the LiDAR sensor is damaged or the LiDAR scan range is restricted, i.e., semi-solid lidars; ii) when objects cannot reflect LiDAR points. We provide visualization of these two failure scenarios in Fig. 4 (a) and evaluate detectors on nuScenes validation set.

Data augmentation for robustness. We propose two data augmentation strategies for above two scenarios: i) we simulate the LiDAR sensor failure situation by setting the points with limited Field-of-View (FOV) in range ( -\pi/3 , \pi/3 ), ( -\pi/2 , \pi/2 ). ii) To simulate the object failure, we use a dropping strategy where each frame has a 0.5 chance of dropping objects, and each object has a 0.5 chance of dropping the LiDAR points inside it. Below, we finetune detectors with these two data augmentation strategies, respectively.

LiDAR sensor failure. The nuScenes dataset provides a Field-of-View (FOV) range of ( -\pi , \pi ) for LiDAR point clouds.
To simulate the LiDAR sensor failure situation, we adopt the first aforementioned robust augmentation strategy in Table 3 .
Obviously, the detector performance degrades as the LiDAR FOV decreases. However, when we fuse camera stream, with the presence of corruptions, the BEVFusion models, in general, are much more robust than their LiDAR-only counterparts, as shown in Fig. 4 (b). Notably, for PointPillars, the mAP increases by 24.4% and 25.1% when LiDAR FOV in ( -\pi/2 , \pi/2 ), ( -\pi/3 , \pi/3 ), respectively. As for TransFusion-L, BEVFusion improves its LiDAR stream by a large margin of over 18.6% mAP and 5.3% NDS.
The vanilla LiDAR-camera fusion approach bai2022transfusion proposed in TransFusion (denoted as LC in Table 3 and Table 4 ) heavily relies on LiDAR data, and the gain is limited to less than 3.3% mAP while NDS is decreased. The results reveal that fusing our camera stream during training and inference compensates for the lack of LiDAR sensors to a substantial extent.

LiDAR fails to receive object reflection points. Here exist common scenarios when LiDAR fails to receive points from the object. For example, on rainy days, the reflection rate of some common objects is below the threshold of LiDAR hence causing the issue of object failure AnonymousBenchmark .
To simulate such a scenario, we adopt the second aforementioned robust augmentation strategy on the validation set. As shown in Table 4 , when we directly evaluate detectors trained without robustness augmentation, BEVFusion shows higher accuracy than the LiDAR-only stream and vanilla LiDAR-camera fusion approach in TransFusion. When we finetune detectors on the robust augmented training set, BEVFusion largely improves PointPillars, CenterPoint, and TransFusion-L by 28.9%, 22.7%, and 15.7% mAP. Specifically, the vanilla LiDAR-camera fusion method in TransFusion has a gain of only 2.6% mAP, which is smaller than the performance before finetuning, we hypothesize the reason is that the lack of foreground LiDAR points brings wrong supervision during training on the augmented dataset. The results reveal that fusing our camera stream during training and inference largely compensates for the lack of object LiDAR points to a substantial extent. We provide visualization in Fig. 4 (b).