Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking (2109.03805v3)

Abstract: Panoptic scene understanding and tracking of dynamic agents are essential for robots and automated vehicles to navigate in urban environments. As LiDARs provide accurate illumination-independent geometric depictions of the scene, performing these tasks using LiDAR point clouds provides reliable predictions. However, existing datasets lack diversity in the type of urban scenes and have a limited number of dynamic object instances which hinders both learning of these tasks as well as credible benchmarking of the developed methods. In this paper, we introduce the large-scale Panoptic nuScenes benchmark dataset that extends our popular nuScenes dataset with point-wise groundtruth annotations for semantic segmentation, panoptic segmentation, and panoptic tracking tasks. To facilitate comparison, we provide several strong baselines for each of these tasks on our proposed dataset. Moreover, we analyze the drawbacks of the existing metrics for panoptic tracking and propose the novel instance-centric PAT metric that addresses the concerns. We present exhaustive experiments that demonstrate the utility of Panoptic nuScenes compared to existing datasets and make the online evaluation server available at nuScenes.org. We believe that this extension will accelerate the research of novel methods for scene understanding of dynamic urban environments.

An Analysis of Panoptic nuScenes: A Comprehensive Benchmark for LiDAR-Based Panoptic Segmentation and Tracking

The paper "Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking" introduces the Panoptic nuScenes dataset, a significant addition to the landscape of autonomous vehicle (AV) datasets. This benchmark extends the conventional 3D object detection paradigm by incorporating tasks like semantic segmentation, panoptic segmentation, and panoptic tracking of LiDAR point clouds. These tasks are crucial for developing comprehensive scene understanding capabilities in AV systems, necessary for navigating complex urban environments.

Motivation and Dataset Composition

The motivation behind Panoptic nuScenes arises from inherent limitations in existing datasets, which often lack diversity in scene types and dynamic object instances, undermining their utility for training robust models. Panoptic nuScenes addresses this shortcoming by offering a richly annotated dataset comprising 1.1 billion points over 1,000 scenes collected from diverse urban settings in Singapore and Boston. This dataset enhances the nuScenes initiative by introducing fine-grained point-wise and temporally consistent annotations covering 32 semantic classes.

Evaluation Metrics and Tasks

The paper proposes novel evaluation metrics tailored to gauge model performance comprehensively. A particular innovation is the Panoptic Tracking (PAT) metric, which balances both segmentation quality and tracking precision, addressing critique areas like track fragmentation. The research underscores the importance of distinct evaluation metrics for holistic scene understanding, furthering the capabilities for AVs to manage a myriad of urban driving scenarios effectively.

Empirical Assessment and Baselines

In empirical assessments, the dataset demonstrates its utility with strong baselines in semantic segmentation, panoptic segmentation, and panoptic tracking. Systems were evaluated based on their accuracy in identifying and temporally associating object instances across frames. EfficientLPS with Kalman Filter emerges as a high-performing baseline, indicating the merits of integrating segmentation with tracking mechanisms.

Implications and Future Directions

The introduction of Panoptic nuScenes aims to catalyze innovation in panoptic tracking methodologies, especially end-to-end solutions. It highlights that current strategies benefit more from task-specific promise than unified end-to-end learning, suggesting areas ripe for future research. The dataset also presents discernible gains from transfer learning, promoting broader generalization across different LiDAR datasets.

Conclusion

Panoptic nuScenes establishes a new benchmark standard in lidar segmentation and tracking, providing a fertile ground for developing advanced AV perception systems. By bridging gaps in existing datasets, it not only enriches semantic granularity but also expands the strategic focus towards robust, temporally consistent tracking in dynamic, real-world urban environments. As future work emerges, we anticipate this dataset to foster significant breakthroughs in autonomous navigation technologies, driven by nuanced, comprehensive scene comprehension.