Cam2BEV
A TensorFlow implementation for generating semantically segmented bird's eye view images from multiple vehicle-mounted cameras using a Sim2Real deep learning approach.
What is Cam2BEV?
Cam2BEV is a TensorFlow-based implementation that computes a semantically segmented bird's eye view image from multiple vehicle-mounted cameras. It solves the problem of accurate distance estimation and environment perception for automated driving by transforming camera perspectives into a unified top-down view with semantic labels. The method uses a Sim2Real deep learning approach, trained on synthetic data to generalize effectively to real-world scenarios.
Researchers and engineers working on perception systems for autonomous vehicles, particularly those focused on camera-based environment understanding and bird's eye view transformation. It's also relevant for computer vision practitioners interested in Sim2Real techniques and semantic segmentation.
Developers choose Cam2BEV because it provides a learned alternative to classical Inverse Perspective Mapping, which distorts 3D objects. It handles occlusions explicitly, supports multiple neural network architectures, and demonstrates strong generalization from synthetic to real data without requiring manually labeled real-world datasets.
Overview
TensorFlow Implementation for Computing a Semantically Segmented Bird's Eye View (BEV) Image Given the Images of Multiple Vehicle-Mounted Cameras.
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