Researchers at Japan’s Ritsumeikan University unveil Dynamic Point-Pixel Feature Alignment Network (DPPFA−Net), a 3D object detection network, combining LiDAR and image data to boost accuracy for robots and self-driving cars, addressing challenges in adverse weather and occlusion.
Traditional 3D object detection methods primarily utilize LiDAR sensors to generate 3D point clouds, but these methods face challenges, especially in adverse weather conditions like rainfall, where LiDAR’s sensitivity to noise becomes a limiting factor.
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