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

arXiv:2312.14919v3 Announce Type: replace-cross
Abstract: Combining complementary sensor modalities is crucial to providing robust perception for safety-critical robotics applications such as autonomous driving (AD). Recent state-of-the-art camera-lidar fusion methods for AD rely on monocular depth estimation which is a notoriously difficult task compared to using depth information from the lidar directly. Here, we find that this approach does not leverage depth as expected and show that naively improving depth estimation does not lead to improvements in object detection performance. Strikingly, …

abstract applications art arxiv autonomous autonomous driving bird cs.cv cs.lg driving fusion information lidar perception replace robotics robust safety safety-critical sensor state transformers type view