  Bird's-Eye View (BEV) features are popular intermediate scene representations
shared by the 3D backbone and the detector head in LiDAR-based object
detectors. However, little research has been done to investigate how to
incorporate additional supervision on the BEV features to improve proposal
generation in the detector head, while still balancing the number of powerful
3D layers and efficient 2D network operations. This paper proposes a novel
scene representation that encodes both the semantics and geometry of the 3D
environment in 2D, which serves as a dense supervision signal for better BEV
feature learning. The key idea is to use auxiliary networks to predict a
combination of explicit and implicit semantic probabilities by exploiting their
complementary properties. Extensive experiments show that our simple yet
effective design can be easily integrated into most state-of-the-art 3D object
detectors and consistently improves upon baseline models.
