  Multi-view camera-based 3D object detection has become popular due to its low
cost, but accurately inferring 3D geometry solely from camera data remains
challenging and may lead to inferior performance. Although distilling precise
3D geometry knowledge from LiDAR data could help tackle this challenge, the
benefits of LiDAR information could be greatly hindered by the significant
modality gap between different sensory modalities. To address this issue, we
propose a Simulated multi-modal Distillation (SimDistill) method by carefully
crafting the model architecture and distillation strategy. Specifically, we
devise multi-modal architectures for both teacher and student models, including
a LiDAR-camera fusion-based teacher and a simulated fusion-based student. Owing
to the ``identical'' architecture design, the student can mimic the teacher to
generate multi-modal features with merely multi-view images as input, where a
geometry compensation module is introduced to bridge the modality gap.
Furthermore, we propose a comprehensive multi-modal distillation scheme that
supports intra-modal, cross-modal, and multi-modal fusion distillation
simultaneously in the Bird's-eye-view space. Incorporating them together, our
SimDistill can learn better feature representations for 3D object detection
while maintaining a cost-effective camera-only deployment. Extensive
experiments validate the effectiveness and superiority of SimDistill over
state-of-the-art methods, achieving an improvement of 4.8\% mAP and 4.1\% NDS
over the baseline detector. The source code will be released at
https://github.com/ViTAE-Transformer/SimDistill.
