UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation

• URL: http://arxiv.org/abs/2308.07732v1
• Date: Tue, 15 Aug 2023 12:13:44 GMT
• Title: UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation
• Authors: Haiyang Wang, Hao Tang, Shaoshuai Shi, Aoxue Li, Zhenguo Li, Bernt Schiele, Liwei Wang
• Abstract summary: We present an efficient multi-modal backbone for outdoor 3D perception named UniTR. UniTR processes a variety of modalities with unified modeling and shared parameters. UniTR is also a fundamentally task-agnostic backbone that naturally supports different 3D perception tasks.
• Score: 113.35352122662752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Jointly processing information from multiple sensors is crucial to achieving accurate and robust perception for reliable autonomous driving systems. However, current 3D perception research follows a modality-specific paradigm, leading to additional computation overheads and inefficient collaboration between different sensor data. In this paper, we present an efficient multi-modal backbone for outdoor 3D perception named UniTR, which processes a variety of modalities with unified modeling and shared parameters. Unlike previous works, UniTR introduces a modality-agnostic transformer encoder to handle these view-discrepant sensor data for parallel modal-wise representation learning and automatic cross-modal interaction without additional fusion steps. More importantly, to make full use of these complementary sensor types, we present a novel multi-modal integration strategy by both considering semantic-abundant 2D perspective and geometry-aware 3D sparse neighborhood relations. UniTR is also a fundamentally task-agnostic backbone that naturally supports different 3D perception tasks. It sets a new state-of-the-art performance on the nuScenes benchmark, achieving +1.1 NDS higher for 3D object detection and +12.0 higher mIoU for BEV map segmentation with lower inference latency. Code will be available at https://github.com/Haiyang-W/UniTR .

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