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Jiayu Zou

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AAAI Conference 2024 Conference Paper

DiffBEV: Conditional Diffusion Model for Bird’s Eye View Perception

Jiayu Zou
Kun Tian
Zheng Zhu
Yun Ye
Xingang Wang

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.

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ICRA Conference 2023 Conference Paper

HFT: Lifting Perspective Representations via Hybrid Feature Transformation for BEV Perception

Jiayu Zou
Zheng Zhu
Junjie Huang 0005
Tian Yang
Guan Huang 0003
Xingang Wang 0003

Restoring an accurate Bird's Eye View (BEV) map plays a crucial role in the perception of autonomous driving. The existing works of lifting representations from frontal view to BEV can be classified into two categories, i. e. , Camera model-Based Feature Transformation (CBFT) and Camera model-Free Feature Transformation (CFFT). We empirically analyze the significant differences between CBFT and CFFT. The former method lift perspective features based on the flat- world assumption, which often causes distortion of regions lying above the ground plane. The latter method is limited in the perception performance due to the absence of geometric priors and time-consuming computing. In this paper, we propose a novel framework with a Hybrid Feature Transformation module (HFT) to lift perspective representations. Furthermore, we design a mutual learning scheme to augment hybrid transformation. The deformable attention mechanism enables the model to pay more attention to relevant regions and capture features with more semantics. We illustrate the effectiveness of HFT in BEV perception tasks, such as segmentation and object detection. Notably, in the task of semantic segmentation, extensive experiments demonstrate that HFT outperforms the previous state-of-the-art method by relatively 17. 9% on the Argoverse and 22. 0% on the KITTI 3D Object dataset. With negligible computing budget, HFT outperforms existing image- based methods on 3D object detection. The code will be released soon.

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