UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation (2308.07732v1)

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 .

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

The paper presents UniTR, a novel multi-modal transformer architecture, designed to enhance 3D perception in autonomous driving. UniTR addresses a critical limitation in current 3D perception models, which typically employ modality-specific paradigms, incurring substantial computation overhead and inefficient collaboration between sensor data. By introducing a unified transformer model to process information from diverse sensors, such as cameras and LiDAR, UniTR aims to streamline multi-modal data processing for Bird's-Eye-View (BEV) representation, a pivotal component for accurate understanding of 3D spaces.

Technical Contributions

UniTR distinguishes itself by implementing a modality-agnostic transformer encoder capable of handling various sensor data in parallel. This approach eschews the conventional modality-specific processing, thereby reducing inference latency and offering a more seamless integration of sensor data. The centerpiece of UniTR's design lies in two innovative transformer blocks, designed to facilitate both intra-modal and inter-modal representation learning:

1. Intra-Modal Transformer Block: This block employs a shared transformer backbone to simultaneously process and learn features specific to each sensor type. By leveraging a dynamic set partitioning strategy within its architecture, UniTR optimizes the parallel feature encoding process, maintaining model efficiency while avoiding separate processing for each data modality.
2. Inter-Modal Transformer Block: Cross-modal feature interaction is achieved through dynamic set partitioning, interfacing distinct features from 2D perspectives and 3D geometrics. This design avoids conventional late-stage fusion steps, integrating data directly within the backbone, and thereby enhancing the efficiency and robustness of the multi-modal features.

Results

UniTR demonstrates state-of-the-art performance, evaluated through benchmarks such as nuScenes, with notable improvements such as a +1.1 NDS for 3D object detection and +12.0 mIoU over previous methods. This performance is achieved alongside reduced inference latency, attributed to the model's comprehensive yet efficient design that combines shared parameters and unified processing.

Implications and Future Perspectives

The UniTR architecture sets a precedent in the development of unified multi-modal transformers, particularly for autonomous driving systems requiring rapid, real-time 3D perception capabilities. The approach underscores a shift towards more integrated and efficient processing frameworks, conducive to practical implementations in real-world scenarios.

Theoretically, this work advances the understanding of unified processing frameworks by successfully applying a single model to handle disparate sensor data, a problem historically compartmentalized in 3D perception research. Practically, the findings could inform future autonomous system designs, focusing on cost and power-efficient hardware that still delivers high-performance perception.

Looking forward, these results prompt further exploration into similar architectures. Future research could delve into refining transformer models to increase robustness against environmental variables and sensor anomalies, potentially diversifying the input sources to include additional sensory modalities like radar. Additionally, examining architecture extensibility, such as transition mechanisms between intra- and inter-modal learning that better adapt to differing operational contexts, remains a promising trajectory.

In summation, UniTR not only pushes the boundaries of current 3D perception methodologies in autonomous vehicles but also illuminates potential pathways towards increasingly unified and efficient deep learning models in artificial intelligence.