arXiv:2411.13628v1 Announce Type: new Abstract: Utilizing temporal information to improve the performance of 3D detection has made great progress recently in the field of autonomous driving. Traditional transformer-based temporal fusion methods suffer from quadratic computational cost and information decay as the length of the frame sequence increases. In this paper, we propose a novel method called MambaDETR, whose main idea is to implement temporal fusion in the efficient state space. Moreover, we design a Motion Elimination module to remove the relatively static objects for temporal fusion. On the standard nuScenes benchmark, our proposed MambaDETR achieves remarkable result in the 3D object detection task, exhibiting state-of-the-art performance among existing temporal fusion methods.
The article “Utilizing Temporal Information for Improved 3D Detection in Autonomous Driving” introduces a novel method called MambaDETR that aims to enhance the performance of 3D detection in autonomous driving. The traditional transformer-based temporal fusion methods face challenges such as high computational cost and information decay with increasing frame sequence length. MambaDETR addresses these issues by implementing temporal fusion in an efficient state space. Additionally, the article presents the Motion Elimination module, which eliminates relatively static objects to improve temporal fusion. The proposed method, MambaDETR, achieves remarkable results in the 3D object detection task on the nuScenes benchmark, surpassing existing temporal fusion methods and demonstrating state-of-the-art performance.
Exploring Temporal Fusion in 3D Object Detection with MambaDETR
Abstract: Utilizing temporal information to improve the performance of 3D detection has made great progress recently in the field of autonomous driving. Traditional transformer-based temporal fusion methods suffer from quadratic computational cost and information decay as the length of the frame sequence increases. In this paper, we propose a novel method called MambaDETR, whose main idea is to implement temporal fusion in the efficient state space. Moreover, we design a Motion Elimination module to remove the relatively static objects for temporal fusion. On the standard nuScenes benchmark, our proposed MambaDETR achieves remarkable result in the 3D object detection task, exhibiting state-of-the-art performance among existing temporal fusion methods.
The world of autonomous driving is constantly evolving, and the ability to accurately detect and track 3D objects in real-time is crucial for ensuring both safety and efficiency. Recent advancements in utilizing temporal information for enhancing 3D object detection have shown promising results, but there are still challenges to overcome.
The Limitations of Traditional Transformer-based Temporal Fusion
One of the main issues with traditional transformer-based temporal fusion methods is the quadratic computational cost and information decay as the length of the frame sequence increases. This limits their effectiveness in real-world scenarios where longer frame sequences are common. These limitations hinder the accuracy and efficiency of 3D object detection systems, as they struggle to maintain high performance while processing increasing amounts of temporal data.
Introducing MambaDETR: A Novel Approach to Temporal Fusion
In order to address these challenges, we propose a novel method called MambaDETR. Our main idea is to implement temporal fusion in the efficient state space. By focusing on the most relevant information in the state space, we can significantly reduce computational cost and minimize information decay. This allows us to maintain high performance even with longer frame sequences.
Furthermore, we introduce a Motion Elimination module in MambaDETR. This module helps remove relatively static objects from the temporal fusion process. By eliminating objects that have minimal motion between frames, we can further optimize the efficiency of the system and improve overall accuracy.
Achieving State-of-the-Art Performance with MambaDETR
We evaluated the performance of MambaDETR on the standard nuScenes benchmark for 3D object detection. Our proposed method achieved remarkable results, exhibiting state-of-the-art performance among existing temporal fusion methods. The efficiency of MambaDETR allows it to handle longer frame sequences without sacrificing accuracy, making it a promising solution for real-world applications in autonomous driving.
Conclusion
MambaDETR offers a new perspective on the problem of temporal fusion in 3D object detection. By leveraging the efficient state space and incorporating a Motion Elimination module, MambaDETR overcomes the limitations of traditional transformer-based methods. Its remarkable performance on the nuScenes benchmark demonstrates its potential for real-world applications in autonomous driving. Moving forward, further research and development in this direction can lead to even more efficient and accurate 3D object detection systems, driving advancements in autonomous driving technology.
The paper titled “MambaDETR: Efficient Temporal Fusion for 3D Object Detection” addresses the challenge of utilizing temporal information to enhance the performance of 3D detection in autonomous driving. This area of research has seen significant advancements in recent years, and the authors propose a novel method to further improve the efficiency and accuracy of temporal fusion.
One of the main issues with existing transformer-based temporal fusion methods is the quadratic computational cost and information decay as the length of the frame sequence increases. This limitation hinders the real-world applicability of these methods, as longer frame sequences are often required for accurate detection in complex driving scenarios. MambaDETR aims to overcome this challenge by implementing temporal fusion in an efficient state space.
The key idea behind MambaDETR is to optimize the temporal fusion process by leveraging an efficient state space. By doing so, the computational cost is significantly reduced compared to traditional methods, making it more feasible to process longer frame sequences in real-time applications. This is a crucial advancement as it allows for more accurate and reliable object detection in autonomous driving scenarios.
Furthermore, the authors introduce a Motion Elimination module in MambaDETR. This module is designed to remove relatively static objects from the temporal fusion process. By eliminating these static objects, the method focuses on capturing the dynamic elements in the scene, which are more informative for accurate object detection. This selective fusion approach helps to improve the overall performance of MambaDETR.
The authors evaluate the proposed method on the standard nuScenes benchmark for 3D object detection. The results demonstrate that MambaDETR achieves remarkable performance, surpassing existing temporal fusion methods and establishing itself as a state-of-the-art solution in this field. This is a significant achievement, as it showcases the effectiveness of the proposed approach and its potential for real-world implementation in autonomous driving systems.
In conclusion, the paper introduces MambaDETR, a novel method for efficient temporal fusion in 3D object detection for autonomous driving. By leveraging an efficient state space and incorporating a Motion Elimination module, MambaDETR addresses the computational cost and information decay limitations of traditional methods. The impressive results on the nuScenes benchmark highlight the potential impact of this research and pave the way for further advancements in temporal fusion techniques for autonomous driving applications.
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