Robo3D: Towards Robust and Reliable 3D Perception against Corruptions (2303.17597v4)

Abstract: The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets often contain data that are meticulously cleaned. Such configurations, however, cannot reflect the reliability of perception models during the deployment stage. In this work, we present Robo3D, the first comprehensive benchmark heading toward probing the robustness of 3D detectors and segmentors under out-of-distribution scenarios against natural corruptions that occur in real-world environments. Specifically, we consider eight corruption types stemming from severe weather conditions, external disturbances, and internal sensor failure. We uncover that, although promising results have been progressively achieved on standard benchmarks, state-of-the-art 3D perception models are at risk of being vulnerable to corruptions. We draw key observations on the use of data representations, augmentation schemes, and training strategies, that could severely affect the model's performance. To pursue better robustness, we propose a density-insensitive training framework along with a simple flexible voxelization strategy to enhance the model resiliency. We hope our benchmark and approach could inspire future research in designing more robust and reliable 3D perception models. Our robustness benchmark suite is publicly available.

• The paper introduces a benchmark that rigorously tests LiDAR-based 3D perception models under various corruption types.
• It categorizes corruptions into weather, external disturbances, and sensor failures, revealing significant model vulnerabilities.
• The study proposes density-insensitive training via a teacher-student design to improve robustness in autonomous driving scenarios.

Essay on Robo3D: Towards Robust and Reliable 3D Perception against Corruptions

The paper "Robo3D: Towards Robust and Reliable 3D Perception against Corruptions" addresses a significant challenge in the field of autonomous driving - the robustness of 3D perception systems when exposed to natural corruptions in real-world environments. The authors present a comprehensive benchmark, Robo3D, which aims to evaluate and enhance the robustness of LiDAR-based 3D perception models against various types of corruptions that these systems may encounter during deployment.

The benchmark includes eight corruption types categorized into three main groups: severe weather conditions, external disturbances, and internal sensor failures. These are further divided into light, moderate, and heavy severity levels. The paper highlights the vulnerability of state-of-the-art 3D perception models to these corruptions, despite their high performance on standard, clean datasets. This indicates a gap between the current capabilities of these models and the needs of real-world applications.

The authors make significant contributions through the development of a robustness evaluation suite across four major 3D perception datasets: SemanticKITTI, KITTI, nuScenes, and Waymo Open. The benchmark not only provides a platform for assessing the resilience of models but also serves as a resource for the community to enhance the robustness of 3D perception systems.

Key observations from the exhaustive experiments conducted include:

1. Sensor Setup Impact: Different LiDAR configurations lead to varying sensitivities to corruptions, impacting the robustness of models trained on data from different sources.
2. Data Representation: The choice of 3D data representation affects robustness, with voxel and point-voxel fusion methods showing superiority over range-view approaches.
3. Task Particularity: The nature of the task, whether detection or segmentation, influences the model's sensitivity to specific corruption types.
4. Augmentation and Regularization: Recent out-of-context augmentation techniques significantly improve robustness, and flexible voxelization strategies contribute to more resilient feature learning.

The paper proposes a novel training framework termed density-insensitive training, which uses a teacher-student model design to enhance the robustness against density variations and point occlusions in 3D data. Experimentation demonstrates this framework's effectiveness in improving model resilience, particularly for corruptions causing data loss.

In conclusion, Robo3D pushes forward the boundary of designing robust 3D perception systems by offering a robust methodology for evaluating and enhancing model performance under real-world corruptions. This work carries substantial implications for the future development of safer, more reliable autonomous systems. As researchers continue to iterate on and utilize the Robo3D benchmark, new methodologies are likely to emerge that will further bridge the gap between controlled experimental conditions and the unpredictable complexities of real-world scenarios in autonomous driving.