面向动态交通场景的3d目标检测

Key Points

• The aim is to address challenges in 3D object detection in dynamic traffic scenarios by improving adaptability and perception of occluded targets.
• Developed an adaptive voxel feature sampling strategy for dynamic scene complexity evaluation.
• Introduced a temporal grouping fusion module for effective spatiotemporal information modeling.
• Utilized an end-to-end approach for adaptive sampling optimization.
• Achieved a 0.8% increase in mean average precision with ResNet50 compared to baseline models.
• Improved nuScenes detection score by 0.8% using the lightweight configuration.
• Further enhanced performance with ResNet101, showing a 1.6% increase in mean average precision and a 2.3% increase in nuScenes detection score.