  BEV perception is of great importance in the field of autonomous driving,
serving as the cornerstone of planning, controlling, and motion prediction. The
quality of the BEV feature highly affects the performance of BEV perception.
However, taking the noises in camera parameters and LiDAR scans into
consideration, we usually obtain BEV representation with harmful noises.
Diffusion models naturally have the ability to denoise noisy samples to the
ideal data, which motivates us to utilize the diffusion model to get a better
BEV representation. In this work, we propose an end-to-end framework, named
DiffBEV, to exploit the potential of diffusion model to generate a more
comprehensive BEV representation. To the best of our knowledge, we are the
first to apply diffusion model to BEV perception. In practice, we design three
types of conditions to guide the training of the diffusion model which denoises
the coarse samples and refines the semantic feature in a progressive way.
What's more, a cross-attention module is leveraged to fuse the context of BEV
feature and the semantic content of conditional diffusion model. DiffBEV
achieves a 25.9% mIoU on the nuScenes dataset, which is 6.2% higher than the
best-performing existing approach. Quantitative and qualitative results on
multiple benchmarks demonstrate the effectiveness of DiffBEV in BEV semantic
segmentation and 3D object detection tasks. The code will be available soon.
