Train in Germany, Test in The USA: Making 3D Object Detectors Generalize (2005.08139v1)

Abstract: In the domain of autonomous driving, deep learning has substantially improved the 3D object detection accuracy for LiDAR and stereo camera data alike. While deep networks are great at generalization, they are also notorious to over-fit to all kinds of spurious artifacts, such as brightness, car sizes and models, that may appear consistently throughout the data. In fact, most datasets for autonomous driving are collected within a narrow subset of cities within one country, typically under similar weather conditions. In this paper we consider the task of adapting 3D object detectors from one dataset to another. We observe that naively, this appears to be a very challenging task, resulting in drastic drops in accuracy levels. We provide extensive experiments to investigate the true adaptation challenges and arrive at a surprising conclusion: the primary adaptation hurdle to overcome are differences in car sizes across geographic areas. A simple correction based on the average car size yields a strong correction of the adaptation gap. Our proposed method is simple and easily incorporated into most 3D object detection frameworks. It provides a first baseline for 3D object detection adaptation across countries, and gives hope that the underlying problem may be more within grasp than one may have hoped to believe. Our code is available at https://github.com/cxy1997/3D_adapt_auto_driving.

Overview of the Paper on Generalization of 3D Object Detectors Across Geographical Domains

The paper "Train in Germany, Test in The USA: Making 3D Object Detectors Generalize" addresses a critical issue in the field of autonomous driving: how well 3D object detection algorithms trained on data from one geographical region perform when applied to data from a different region. The research primarily focuses on adapting 3D object detectors using data from various datasets collected in different global locations, accompanied by different sensor configurations. This work leverages datasets such as KITTI, Argoverse, nuScenes, Lyft, and Waymo, which offer diversity in geo-locations and sensing devices.

Core Contributions

The authors identify a substantial challenge: significant performance degradation when transferring 3D object detectors trained in one domain (e.g., a city or country) to another. Notably, the paper highlights a surprising realization: a major barrier to adaptation is the statistical difference in car sizes across these datasets, which inherently represent different urban environments.

Key contributions of the paper include:

1. Identification of the Primary Adaptation Hurdle: The research points out that the distinctive challenge in cross-dataset adaptation lies in the variations of car sizes rather than disparities in sensing technologies.
2. Statistical Normalization Approach: The authors propose a simple yet effective adjustment technique based on the mean car sizes in the target domain, applied to both training labels and input signals. This technique significantly mitigates domain gap effects and improves detector robustness across datasets.
3. Empirical Validation: The paper conducts comprehensive experiments using popular 3D object detection models, such as PointRCNN and PIXOR. Quantitative evaluations demonstrate amending the car sizes alone can considerably enhance cross-domain performance.

Quantitative Results and Analysis

The paper presents extensive testing outcomes that underscore the challenges faced with naïve cross-domain application. For instance, a PointRCNN model trained on the KITTI dataset performed 36% worse when applied to the Waymo dataset. The statistics indicate that a simple correction using average car sizes from public resources can close a significant portion of this performance gap, pointing to a 41.4% gain in 3D detection for easy cases upon adjustment.

Implications and Future Directions

The findings of this paper suggest a pivotal rethinking in the development and deployment of autonomous vehicle perception systems. The ease of incorporating mean size statistics as a normalized feature signals a pivotal direction in improving generalization capabilities of AI models for autonomous driving. By addressing the size variance, researchers and industries can develop better models that are pragmatic and operationally viable across heterogeneous urban spaces.

Moreover, the results advocate for a closer examination of other latent factors potentially impacting localization performance across different environmental contexts, beyond mere car size statistics. As the field progresses, future research might benefit from exploring adaptive techniques in the representation learning phase, possibly utilizing advanced transfer learning or domain adaptation methodologies.

In summary, this paper demonstrates that by fine-tuning certain key aspects of 3D object detection frameworks, like integrating accessible real-world aggregate size data, the adaptation performance of these frameworks can be significantly enhanced, thus promising safer deployment of autonomous vehicles worldwide.