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If you are using this image for a task instead of autonomous driving, you should focus on Spatial Arrangement and Region Proposal Networks (RPN) to identify text blocks and headers. If you'd like to dive deeper into this topic:

Potential cyclists or moving traffic in the foreground.

We apply a projection technique often utilized in architectures like BirdNet+ or PointPillars .

Road surface estimation to set the "ground truth" for the 3D grid. 4. Conclusion

This paper explores the challenges of accurate 3D bounding box estimation in complex urban traffic scenarios. Using the KITTI benchmark image as a representative sample, we analyze the integration of LiDAR point clouds with RGB camera data to improve vehicle and pedestrian detection in high-occlusion environments. 1. Introduction

Residential/Urban street with parked and moving vehicles. Key Challenge: Accurately predicting the coordinates and dimensions of objects from a single perspective. 2. Methodology