3D Perception Task

3D perception tasks, aiming to reconstruct and understand 3D scenes from various sensor inputs (e.g., cameras, LiDAR), are a crucial area of research in computer vision and robotics. Current efforts focus on improving the efficiency and accuracy of 3D perception models, often employing transformer-based architectures and leveraging multi-modal data fusion techniques, including the use of semantic and depth information as priors. These advancements are driving progress in autonomous driving, robotics, and other applications requiring robust 3D scene understanding, particularly through the development of self-supervised and multi-task learning approaches to overcome data limitations and computational challenges. The resulting improvements in accuracy and efficiency are significant for real-world deployment.