MAESTRO: Task-Relevant Optimization via Adaptive Feature Enhancement and Suppression for Multi-task 3D Perception

Multi-task learning (MTL) has emerged as a promising approach to jointly optimize multiple perception tasks in autonomous driving, but existing methods suffer from feature interference and inefficient task-specific learning. In this paper, we introduce MAESTRO, a novel query-based framework that explicitly generates task-specific features to mitigate feature interference and improve efficiency in multi-task 3D perception. Our model consists of three key components: Semantic Query Generator (SQG), Task-Specific Feature Generator (TSFG), and Scene Query Aggregator (SQA). SQG generates query features and decomposes them into foreground and background queries to facilitate selective feature sharing. TSFG refines task-specific features by integrating decomposed queries with voxel features while suppressing irrelevant information. The detection and map heads generate task-aware queries, which SQA aggregates with the initially extracted queries from SQG to enhance semantic occupancy prediction. Extensive evaluations on the nuScenes benchmark show that MAESTRO achieves state-of-the-art performance across all tasks. Our model overcomes the performance trade-off among tasks in multi-task learning, where improving one task often hinders others, and sets a new benchmark in multi-task 3D perception.