Multi Task 3D

Multi-task 3D perception aims to simultaneously perform multiple 3D understanding tasks, such as object detection and semantic segmentation, from various sensor inputs (e.g., cameras, lidar). Current research focuses on developing efficient and robust model architectures that avoid task interference, often employing unified representations like vector fields or hybrid encoding strategies to process data from multiple modalities. These advancements are crucial for autonomous driving and robotics, enabling more reliable and computationally efficient 3D scene understanding for improved navigation and decision-making. The development of effective multi-task learning frameworks is driving significant improvements in the accuracy and efficiency of 3D perception systems.