  In this paper, we propose PETRv2, a unified framework for 3D perception from
multi-view images. Based on PETR, PETRv2 explores the effectiveness of temporal
modeling, which utilizes the temporal information of previous frames to boost
3D object detection. More specifically, we extend the 3D position embedding (3D
PE) in PETR for temporal modeling. The 3D PE achieves the temporal alignment on
object position of different frames. A feature-guided position encoder is
further introduced to improve the data adaptability of 3D PE. To support for
multi-task learning (e.g., BEV segmentation and 3D lane detection), PETRv2
provides a simple yet effective solution by introducing task-specific queries,
which are initialized under different spaces. PETRv2 achieves state-of-the-art
performance on 3D object detection, BEV segmentation and 3D lane detection.
Detailed robustness analysis is also conducted on PETR framework. We hope
PETRv2 can serve as a strong baseline for 3D perception. Code is available at
\url{https://github.com/megvii-research/PETR}.
