DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds (2306.06023v2)

Abstract: Existing offboard 3D detectors always follow a modular pipeline design to take advantage of unlimited sequential point clouds. We have found that the full potential of offboard 3D detectors is not explored mainly due to two reasons: (1) the onboard multi-object tracker cannot generate sufficient complete object trajectories, and (2) the motion state of objects poses an inevitable challenge for the object-centric refining stage in leveraging the long-term temporal context representation. To tackle these problems, we propose a novel paradigm of offboard 3D object detection, named DetZero. Concretely, an offline tracker coupled with a multi-frame detector is proposed to focus on the completeness of generated object tracks. An attention-mechanism refining module is proposed to strengthen contextual information interaction across long-term sequential point clouds for object refining with decomposed regression methods. Extensive experiments on Waymo Open Dataset show our DetZero outperforms all state-of-the-art onboard and offboard 3D detection methods. Notably, DetZero ranks 1st place on Waymo 3D object detection leaderboard with 85.15 mAPH (L2) detection performance. Further experiments validate the application of taking the place of human labels with such high-quality results. Our empirical study leads to rethinking conventions and interesting findings that can guide future research on offboard 3D object detection.

• The paper presents a dual strategy combining multi-frame detection with offline tracking to generate complete and accurate object trajectories.
• The paper proposes an attribute-based refining module that bypasses motion state dependency to effectively utilize long-term sequential data.
• The paper demonstrates robust performance with an 85.15 mAPH on the Waymo leaderboard, underscoring enhanced 3D detection capabilities.

DetZero: Advancements in Offboard 3D Object Detection

The paper "DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds" introduces a significant advancement in the context of offboard 3D object detection systems, specifically by proposing a new paradigm aimed at leveraging the full potential of long-term sequential point clouds. The DetZero approach addresses critical limitations in existing methodologies, particularly regarding the generation of complete object tracks and the effective utilization of temporal contextual information.

Current offboard 3D detectors often rely on modular pipelines where sequential point clouds are employed without stringent constraints on model capacity and inference speed. However, the authors identify two pivotal hurdles that hinder optimal performance in offboard 3D detectors: the insufficient generation of complete object trajectories and the challenges posed by the motion states during object-centric refining stages.

DetZero innovatively resolves these challenges by utilizing a two-fold strategy. First, the design incorporates a multi-frame detector alongside an offline tracker, significantly enhancing the completeness and accuracy of the generated object tracks. This configuration is vital as incomplete tracks can severely impede the generation of effective object-specific temporal point cloud data. Secondly, the development of an attribute-based refining module—which eschews motion state dependency in favor of capturing common object attributes—enables more precise and effective leveraging of long-term sequential data.

Empirical results obtained from extensive experiments on the Waymo Open Dataset (WOD) demonstrate that DetZero considerably outperforms state-of-the-art methods, both onboard and offboard, in 3D detection tasks. Notably, DetZero achieves top rankings on the Waymo 3D object detection leaderboard with a detection performance of 85.15 mAPH (L2). These results underscore the potential of DetZero to produce high-quality automated labels, which could replace manual annotations in certain contexts, thus reducing labor costs associated with data labeling.

From a methodological perspective, DetZero introduces a novel decomposition of the traditional bounding box regression into three distinct modules, each predicting respective object attributes: geometry, position, and confidence. This modular approach not only enhances the network's specialization in object representation learning but also facilitates better integration and exploitation of temporal sequences, which are crucial for refining detection outcomes.

The paper's contribution is underscored by the extensive ablation studies described, which validate the impact of each component in the DetZero pipeline. These studies, along with cross-evaluation results, delineate the effectiveness of each module and underscore the benefits of offline tracking in generating continuous object trajectories from detection data.

Looking forward, the implications of DetZero for real-world applications are significant. Its capacity to handle long-term sequential data with improved accuracy and robustness offers promising avenues for enhanced autonomous driving systems and potentially other domains requiring high-fidelity 3D object detection. As AI progresses, future work could explore integrating DetZero's principles with other sensor modalities to further augment 3D perception capabilities. Additionally, further research may investigate adaptive learning mechanisms, enabling DetZero to operate efficiently across diverse environments and scenarios.

In summary, DetZero represents a substantial stride forward in offboard 3D object detection technology, offering a well-founded methodology that overcomes previous limitations while setting a new benchmark for performance. Its potential contributions to the field are both practical and theoretical, paving the way for the continued evolution of AI-driven perception systems.