  To achieve accurate and low-cost 3D object detection, existing methods
propose to benefit camera-based multi-view detectors with spatial cues provided
by the LiDAR modality, e.g., dense depth supervision and bird-eye-view (BEV)
feature distillation. However, they directly conduct point-to-point mimicking
from LiDAR to camera, which neglects the inner-geometry of foreground targets
and suffers from the modal gap between 2D-3D features. In this paper, we
propose the learning scheme of Target Inner-Geometry from the LiDAR modality
into camera-based BEV detectors for both dense depth and BEV features, termed
as TiG-BEV. First, we introduce an inner-depth supervision module to learn the
low-level relative depth relations between different foreground pixels. This
enables the camera-based detector to better understand the object-wise spatial
structures. Second, we design an inner-feature BEV distillation module to
imitate the high-level semantics of different keypoints within foreground
targets. To further alleviate the BEV feature gap between two modalities, we
adopt both inter-channel and inter-keypoint distillation for feature-similarity
modeling. With our target inner-geometry distillation, TiG-BEV can effectively
boost BEVDepth by +2.3% NDS and +2.4% mAP, along with BEVDet by +9.1% NDS and
+10.3% mAP on nuScenes val set. Code will be available at
https://github.com/ADLab3Ds/TiG-BEV.
