HFT: Lifting Perspective Representations via Hybrid Feature Transformation

Understand

Autonomous driving requires accurate and detailed Bird's Eye View (BEV) semantic segmentation for decision making, which is one of the most challenging tasks for high-level scene perception.

• Feature transformation from frontal view to BEV is the pivotal technology for BEV semantic segmentation.
• Existing works can be roughly classified into two categories, i.e., Camera model-Based Feature Transformation (CBFT) and Camera model-Free Feature Transformation (CFFT).
• In this paper, we empirically analyze the vital differences between CBFT and CFFT.