Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection (1908.09492v1)

Abstract: This report presents our method which wins the nuScenes3D Detection Challenge [17] held in Workshop on Autonomous Driving(WAD, CVPR 2019). Generally, we utilize sparse 3D convolution to extract rich semantic features, which are then fed into a class-balanced multi-head network to perform 3D object detection. To handle the severe class imbalance problem inherent in the autonomous driving scenarios, we design a class-balanced sampling and augmentation strategy to generate a more balanced data distribution. Furthermore, we propose a balanced group-ing head to boost the performance for the categories withsimilar shapes. Based on the Challenge results, our methodoutperforms the PointPillars [14] baseline by a large mar-gin across all metrics, achieving state-of-the-art detection performance on the nuScenes dataset. Code will be released at CBGS.

• The paper presents a novel class-balanced sampling strategy that mitigates long-tailed class distributions by duplicating underrepresented samples.
• It introduces a multi-group head network that leverages multi-task learning to share features among similar classes while reducing interference.
• Experiments on the nuScenes dataset achieve a state-of-the-art mAP of 52.8%, significantly enhancing detection for rare classes like Bicycle and Motorcycle.

Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

The paper presented introduces a method designed to address significant challenges in 3D object detection, specifically focusing on class imbalance prevalent in autonomous vehicle datasets such as nuScenes. The methodological advancements demonstrated in this work have resulted in outperforming existing benchmarks, particularly PointPillars, on the nuScenes dataset, which represents a modern standard for this research area.

The core contribution of the paper lies in the development of a class-balanced sampling and augmentation strategy. This approach is well-justified given the observed long-tailed distribution of instances in the nuScenes dataset, where the distribution of object instances is heavily skewed towards a few categories. By employing a sampling technique reminiscent of those used in image classification, the authors effectively re-weight the dataset to provide a more uniform distribution across classes. This class balancing is achieved by duplicating samples from underrepresented classes, thereby enhancing the dataset to over four times its original size to mitigate the imbalance.

Additionally, the paper introduces a novel multi-group head network architecture that leverages the principle of multi-task learning. By grouping similar object classes based on shape or size and implementing a specialized grouping head for similar categories, the detection performance on rare classes is improved. This class-balanced grouping allows for shared feature learning among similar classes while reducing interference between classes of different shapes and sizes. This design effectively aids in addressing the model's dominance by major classes, thereby improving detection for tail classes without compromising overall accuracy.

The methodology section in the paper delineates the precise implementation details, including the use of sparse 3D convolutional networks for feature extraction. This is complemented by a carefully configured region proposal network and enhancements to the loss function, which includes orientation classification adjustments to address orientation ambiguities inherent in many 3D datasets.

Numerical results validate the proposed methods extensively. The authors report state-of-the-art performance in the nuScenes 3D Detection Challenge with a mean average precision (mAP) of 52.8%, surpassing the performance of the PointPillars baseline by a significant margin. These results are across all categories, indicating the robustness of their approach to handle a wide range of object classes. There is a pronounced improvement, notably for minor classes such as "Bicycle" and "Motorcycle," where enhancements are observed by an order of magnitude.

In terms of practical implications, the advancements presented could significantly impact the deployment of 3D object detection systems in real-world applications, such as autonomous driving. The improved ability to detect rare objects accurately is critical for safety and operational efficiency in autonomous systems. The class-balanced methodology not only addresses current limitations in datasets like nuScenes but also sets a precedent for future dataset designs and annotation practices to consider class imbalance proactively.

Looking towards future work, the exploration of additional datasets or scenarios using this method could provide further insights, especially concerning datasets with even more extreme class imbalance or those incorporating additional modalities. Additionally, integrating these techniques with other leading 3D detection frameworks could present opportunities for further hybrid innovations.

By providing open source access to their code, the authors facilitate continued progress and validation from the larger research community on these preliminary yet promising findings. The contribution from this paper offers a compelling strategy for addressing class imbalance issues, a challenge prevalent in many 3D object detection and classification contexts.