BEVFormer

Why we included this project

BEVFormer is the official implementation of the ECCV 2022 paper that showed how to learn bird's-eye-view representations from camera images using spatiotemporal transformers. The codebase takes feeds from multiple cameras and merges them into one BEV feature space, which then supports both 3D object detection and semantic map segmentation. Because both tasks share the same learned representation, you can evaluate them together instead of running separate pipelines. It ships pretrained model configs, training and inference scripts for the nuScenes benchmark, and documented results to reproduce, which makes it a practical baseline and a common research starting point. The catch is that it is research-grade code tied to a paper, so expect to sort out dependencies and tuning before it runs cleanly; it is not production-ready software.