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Combining complementary sensor modalities is crucial to providing robustperception for safety-critical robotics applications such as autonomous driving(AD). Recent state-of-the-art camera-lidar fusion methods for AD rely onmonocular depth estimation which is a notoriously difficult task compared tousing depth information from the lidar directly. Here, we find that thisapproach does not leverage depth as expected and show that naively improvingdepth estimation does not lead to improvements in object detection performanceand that, strikingly, removing depth estimation altogether does not degradeobject detection performance. This suggests that relying on monocular depthcould be an unnecessary architectural bottleneck during camera-lidar fusion. Inthis work, we introduce a novel fusion method that bypasses monocular depthestimation altogether and instead selects and fuses camera and lidar featuresin a bird's-eye-view grid using a simple attention mechanism. We show that ourmodel can modulate its use of camera features based on the availability oflidar features and that it yields better 3D object detection on the nuScenesdataset than baselines relying on monocular depth estimation.

Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers