LiDAR Based Multi Task

LiDAR-based multi-task learning aims to improve the efficiency and accuracy of autonomous driving perception by jointly performing multiple tasks, such as 3D object detection, semantic segmentation, and motion estimation, within a single neural network. Current research focuses on developing efficient architectures, often employing transformer-based or convolutional neural networks with shared encoders and task-specific decoders, sometimes incorporating novel modules like attention mechanisms or semantic weighting to enhance performance. This approach offers significant advantages in computational cost and resource utilization compared to separate single-task models, leading to faster and more robust perception systems for applications like autonomous vehicles and robotics.