FUTR3D: A Unified Sensor Fusion Framework for 3D Detection (2203.10642v2)

Abstract: Sensor fusion is an essential topic in many perception systems, such as autonomous driving and robotics. Existing multi-modal 3D detection models usually involve customized designs depending on the sensor combinations or setups. In this work, we propose the first unified end-to-end sensor fusion framework for 3D detection, named FUTR3D, which can be used in (almost) any sensor configuration. FUTR3D employs a query-based Modality-Agnostic Feature Sampler (MAFS), together with a transformer decoder with a set-to-set loss for 3D detection, thus avoiding using late fusion heuristics and post-processing tricks. We validate the effectiveness of our framework on various combinations of cameras, low-resolution LiDARs, high-resolution LiDARs, and Radars. On NuScenes dataset, FUTR3D achieves better performance over specifically designed methods across different sensor combinations. Moreover, FUTR3D achieves great flexibility with different sensor configurations and enables low-cost autonomous driving. For example, only using a 4-beam LiDAR with cameras, FUTR3D (58.0 mAP) achieves on par performance with state-of-the-art 3D detection model CenterPoint (56.6 mAP) using a 32-beam LiDAR.

• The paper introduces FUTR3D, a unified sensor fusion framework that leverages a modality-agnostic feature sampler and transformer decoder to streamline 3D detection.
• The methodology eliminates sensor-specific heuristics by employing end-to-end processing, achieving high accuracy even with low-cost sensor setups like a 4-beam LiDAR and cameras.
• Empirical results on the NuScenes dataset show FUTR3D attaining 58.0 mAP, outperforming traditional methods and highlighting its potential for efficient autonomous perception.

Unified Sensor Fusion Framework for 3D Detection: An Evaluation of FUTR3D

Introduction

Sensor fusion stands as a critical area of focus in autonomous perception systems, especially in domains such as autonomous driving and robotics. The paper presents FUTR3D, the first unified end-to-end framework designed for 3D object detection that operates under virtually any sensor configuration. Unlike traditional methods that rely on sensor-specific heuristics and post-processing, FUTR3D uses a Modality-Agnostic Feature Sampler (MAFS) with a transformer decoder to achieve superior flexibility and performance across various sensor combinations. This paper evaluates FUTR3D's effectiveness in assimilating information from 2D cameras, 3D LiDARs, and 4D imaging radars.

Methodological Approach

FUTR3D introduces an innovative approach to multi-modal sensor fusion by incorporating a query-based MAFS. This component ensures that feature sampling is conducted in a unified domain, making the model both end-to-end and modality-agnostic. By avoiding late-stage fusion and post-processing, FUTR3D simplifies the fusion process without sacrificing accuracy.

The architecture updates object queries using an iterative refinement method, leveraging self-attention mechanisms to sample from multi-scale feature maps across different modalities. This allows the framework to accommodate various sensor combinations, including setups with limited sensors like a 4-beam LiDAR coupled with cameras.

Empirical Performance

The empirical results of FUTR3D are particularly strong. On the NuScenes dataset, it outperforms conventional sensor-specific 3D detection methods across different configurations:

• FUTR3D achieves 58.0 mAP using a 4-beam LiDAR and cameras, surpassing a state-of-the-art model with a 32-beam LiDAR by 1.4 mAP.
• FUTR3D not only excels in multi-modal fusion but also in single-modality settings, yielding competitive results.

This performance underscores the framework's potential to provide low-cost alternatives while maintaining high levels of detection accuracy.

Theoretical and Practical Implications

Theoretically, FUTR3D's introduction of a modality-agnostic feature sampler expands the understanding of sensor fusion by eliminating sensor dependence in feature processing. This freedom from sensor-specific configurations enables greater innovation and adaptation in 3D detection algorithms.

Practically, FUTR3D's flexibility facilitates its application across a broad array of environments with varying sensor setups. This is particularly relevant for autonomous vehicles, where cost-effective and adaptable sensor fusion solutions can balance performance with economic and operational constraints.

Potential Limitations and Future Work

A notable limitation of FUTR3D's methodology is its reliance on a two-stage training process, involving separate pre-training of camera and LiDAR encoders before joint fine-tuning. Streamlining this training process presents a potential area for further research. Additionally, while the framework is notably robust, there may still be edge cases, such as highly occluded environments, that challenge its performance.

Future development could explore more efficient training methodologies and the extension of FUTR3D to new types of sensors, potentially broadening its applicability and easing its integration into various technological ecosystems. Moreover, additional research could focus on optimizing the computational demands of the framework, thereby enhancing its feasibility for real-world deployment in resource-constrained settings.

Conclusion

FUTR3D offers a promising advancement in sensor fusion frameworks for 3D detection, providing a unified method that achieves high performance across diverse sensor combinations. Its flexible architecture positions it as a foundational solution for future research and development in autonomous perception systems, emphasizing both cost efficiency and detection robustness. The introduction of a modality-agnostic approach in FUTR3D lays the groundwork for innovative multi-modal fusion techniques, potentially steering future directions in both academic and practical applications related to sensor-based perception technologies.