Detection Framework

3D object detection frameworks are being actively developed to improve the accuracy and efficiency of object identification in various applications, such as autonomous driving and robotics. Current research focuses on enhancing robustness across diverse datasets, leveraging multi-sensor fusion (e.g., LiDAR and camera data), and optimizing model architectures for speed and resource efficiency, including convolutional-only approaches and the incorporation of techniques like knowledge distillation and semantic awareness. These advancements are crucial for enabling reliable real-time perception in complex environments, impacting fields ranging from autonomous vehicle navigation to medical image analysis.