Object as Query: Lifting any 2D Object Detector to 3D Detection (2301.02364v3)

Abstract: 3D object detection from multi-view images has drawn much attention over the past few years. Existing methods mainly establish 3D representations from multi-view images and adopt a dense detection head for object detection, or employ object queries distributed in 3D space to localize objects. In this paper, we design Multi-View 2D Objects guided 3D Object Detector (MV2D), which can lift any 2D object detector to multi-view 3D object detection. Since 2D detections can provide valuable priors for object existence, MV2D exploits 2D detectors to generate object queries conditioned on the rich image semantics. These dynamically generated queries help MV2D to recall objects in the field of view and show a strong capability of localizing 3D objects. For the generated queries, we design a sparse cross attention module to force them to focus on the features of specific objects, which suppresses interference from noises. The evaluation results on the nuScenes dataset demonstrate the dynamic object queries and sparse feature aggregation can promote 3D detection capability. MV2D also exhibits a state-of-the-art performance among existing methods. We hope MV2D can serve as a new baseline for future research. Code is available at \url{https://github.com/tusen-ai/MV2D}.

• The paper presents a novel dynamic query generation method that transforms 2D detections into adaptive 3D queries for improved localization.
• It employs a sparse cross-attention mechanism to refine feature aggregation by focusing on key image areas and suppressing irrelevant noise.
• Evaluated on nuScenes, the framework achieves significant performance gains, improving mean Average Precision by up to 5% over state-of-the-art methods.

Insightful Overview of "Object as Query: Lifting any 2D Object Detector to 3D Detection"

The paper "Object as Query: Lifting any 2D Object Detector to 3D Detection" presents a novel framework, Multi-View 2D Objects guided 3D Object Detector (MV2D), aimed at enhancing 3D object detection using existing 2D object detectors. The primary motivation lies in exploiting the rich semantic information that 2D detectors can provide as priors for 3D detection tasks. This approach is particularly significant in the context of vision-based 3D object detection, which has traditionally grappled with efficiently utilizing geometric configurations and multi-view correspondences.

Methodological Innovation

1. Dynamic Query Generation: The core innovation of MV2D is to utilize 2D object detections to generate dynamic 3D object queries. Unlike fixed query-based methods, this dynamic approach is more adaptable, leveraging 2D semantic cues to inform 3D object localization. This step is crucial in overcoming the limitations of existing query-based methods which often rely on empirically distributed fixed queries in 3D space, leading to inefficiencies and potential inaccuracies in dynamic environments.
2. Design of Sparse Cross-Attention: The paper introduces a sparse cross-attention mechanism to focus queries on relevant image features, thus suppressing noise and distractions from non-object regions. This allows for more precise feature aggregation, enhancing the clarity and accuracy of the 3D detection results.
3. Integration and Training: The MV2D framework integrates seamlessly with any 2D object detection component. The use of pretrained weights and a unified training approach for both 2D and 3D components ensures efficient learning, leveraging advancements in 2D object detection.

Experimental Results

The performance of MV2D is rigorously evaluated on the nuScenes dataset, a benchmark for autonomous vehicle perception. MV2D demonstrates superior performance compared to existing state-of-the-art methods. Notably, it achieves significant gains in both Mean Average Precision (mAP) and Normalized Distance Score (NDS), showing improvements of up to 5% in mAP compared to prominent methods like PETRv2. These results underscore the efficacy of using 2D detections to inform and enhance 3D object localization.

Practical Implications

From a practical perspective, MV2D's approach offers a versatile and scalable solution to 3D object detection in multifarious real-world applications, such as autonomous driving and robotic perception. The ability to lift any 2D detector to a 3D context not only extends the utility of existing 2D algorithms but also allows for seamless integration within current industry workflows, facilitating quicker deployment and adoption.

Theoretical Implications and Future Directions

Theoretically, MV2D opens up new avenues for research in multi-view 3D object detection by highlighting the symbiotic potential between 2D and 3D understanding. Future research might focus on further enhancing this integration, perhaps by leveraging more sophisticated forms of attention mechanisms or exploring other facets of 2D semantics that could inform 3D perception.

In conclusion, the MV2D framework represents a significant step forward in the field of 3D object detection, emphasizing the value of interconnectedness between 2D and 3D domains. Its robust performance and versatile applicability make it a strong candidate for becoming a new baseline in multi-view 3D detection research.