Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels (2004.04091v1)

Abstract: Point cloud analysis has received much attention recently; and segmentation is one of the most important tasks. The success of existing approaches is attributed to deep network design and large amount of labelled training data, where the latter is assumed to be always available. However, obtaining 3d point cloud segmentation labels is often very costly in practice. In this work, we propose a weakly supervised point cloud segmentation approach which requires only a tiny fraction of points to be labelled in the training stage. This is made possible by learning gradient approximation and exploitation of additional spatial and color smoothness constraints. Experiments are done on three public datasets with different degrees of weak supervision. In particular, our proposed method can produce results that are close to and sometimes even better than its fully supervised counterpart with 10$\times$ fewer labels.

• The paper introduces a weak supervision approach that reduces labeling requirements by 10× while maintaining competitive segmentation accuracy.
• It leverages PointNet++ to achieve comparable performance on datasets like ShapeNet and S3DIS with minimal annotations.
• The method highlights practical benefits for resource-intensive labeling and paves the way for future research in sustainable data-driven AI.

Weakly Supervised Semantic Point Cloud Segmentation: Towards 10× Fewer Labels

The paper entitled "Weakly Supervised Semantic Point Cloud Segmentation: Towards 10× Fewer Labels" addresses the challenge of reducing the amount of labeled data required for effective semantic segmentation in point cloud datasets. The research focuses on the implementation of weakly supervised learning techniques, aiming for a tenfold reduction in label dependency while maintaining or achieving competitive accuracy in segmentation tasks.

Methodology and Results

The authors explore weak supervision using different label amounts: full supervision, a single labeled point per category (1pt WeakSup), and 10% labeled samples (10% WeakSup). PointNet++, a notable point cloud segmentation network, serves as the base encoder for experiments conducted on the ShapeNet and S3DIS datasets. The evaluation reveals that even with sparse annotations, the proposed weakly supervised model achieves results comparable to fully supervised models.

A quantitative analysis is presented in Table 1 where PointNet++ under full supervision achieves a category average of 81.87 on ShapeNet. Remarkably, with only 10% labeled data, the network maintains a category average of 81.27—indicating a negligible drop in performance. Likewise, with only 1 labeled point per category, the category average remains robust at 80.82. These results exemplify that the weak supervision approach is proficient in handling various virtual prototypes with minimal performance degradation.

The PartNet dataset evaluation, delineated in Table 2, demonstrates similar trends. Here, the full supervision category average from PointNet++ is noted at 65.5, while the 10% WeakSup achieves 64.5, and 1pt WeakSup results in 54.6. This pattern confirms the potential of reduced label requirements while preserving segmentation performance across diverse object types.

Qualitative Examples

The inclusion of qualitative examples on datasets such as S3DIS and ShapeNet underscores the practical impacts of this methodology. Figures illustrate comparable segmentation outputs between full and weak supervision modes. Particularly on the ShapeNet dataset, the margin between weak and full supervision is remarkably small, further establishing the viability of the approach.

Implications and Future Directions

This research holds significant practical implications in scenarios where data labeling is resource-intensive or unfeasible. The strategic reduction in labeled data requirements without sacrificing segmentation accuracy could revolutionize data annotation processes in large-scale machine learning applications.

Theoretically, the success of weak supervision exemplifies a pathway for further exploration in domain adaptation and transfer learning in semantic segmentation. As AI applications increasingly require efficiency in training data utilization, the introduction of such weakly supervised models may prompt a shift towards more sustainable data-driven AI techniques.

Further developments in encoder networks and weak supervision strategies could enhance adaptability across various datasets and tasks. The examination of alternative network architectures like DGCNN with similar methodologies may yield insights into optimizing neural representations further. Future research could also investigate the applicability of weak supervision frameworks in more complex, real-world environments beyond synthesized datasets like ShapeNet and S3DIS.

In summary, the paper introduces a robust and pathbreaking approach to semantic segmentation in point clouds, demonstrating the practicality of weak supervision and its potential to economize the labeling process substantially while achieving competitive performance in segmentation tasks.