SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds (2104.04891v3)

Abstract: Labelling point clouds fully is highly time-consuming and costly. As larger point cloud datasets with billions of points become more common, we ask whether the full annotation is even necessary, demonstrating that existing baselines designed under a fully annotated assumption only degrade slightly even when faced with 1% random point annotations. However, beyond this point, e.g., at 0.1% annotations, segmentation accuracy is unacceptably low. We observe that, as point clouds are samples of the 3D world, the distribution of points in a local neighborhood is relatively homogeneous, exhibiting strong semantic similarity. Motivated by this, we propose a new weak supervision method to implicitly augment highly sparse supervision signals. Extensive experiments demonstrate the proposed Semantic Query Network (SQN) achieves promising performance on seven large-scale open datasets under weak supervision schemes, while requiring only 0.1% randomly annotated points for training, greatly reducing annotation cost and effort. The code is available at https://github.com/QingyongHu/SQN.

• The paper introduces SQN, which leverages sparse annotations to achieve competitive 3D segmentation performance while reducing labeling costs by up to 98%.
• It employs a novel point neighborhood query strategy and hierarchical feature extraction to propagate weak labels effectively across complex scenes.
• Empirical evaluations on datasets like S3DIS and Semantic3D demonstrate SQN's ability to maintain high mIoU with minimal supervision.

Essay on "SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds"

The paper "SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds" addresses the formidable challenge of semantic segmentation in the context of 3D point clouds with limited labeled data. Despite the steady advancements propelled by fully-supervised learning approaches in recent years, the significant labor involved in annotating point clouds necessitates the exploration of efficient weakly-supervised solutions. This paper introduces the Semantic Query Network (SQN), which significantly reduces annotation requirements by leveraging weakly-labeled data for training.

Overview of SQN and Motivation

The primary motivation for this research lies in the prohibitive cost of annotating large-scale point cloud datasets. Traditional methods rely heavily on fully annotated datasets, an impractical necessity for extensive 3D scenes such as whole cities or vast architectural diagrams. Recognizing the inherent local semantic similarity within point clouds, the authors propose a weak supervision method that capitalizes on these properties to reduce manual labeling efforts while maintaining high segmentation accuracy.

The proposed SQN employs a novel point neighborhood query strategy and hierarchical latent feature representation to extend the influence of sparse annotation across the point cloud. This implicit augmentation of sparse training signals through semantic queries enables the SQN to achieve competitive segmentation results with minimal annotated points.

Technical Highlights and Findings

The SQN demonstrates its efficacy across multiple open datasets, achieving strong performance while being trained with merely 0.1% of the points labeled. This reduction in required labels represents a significant leap forward in terms of annotation cost efficiency. Comparative benchmarks illustrate that SQN not only sustains performance comparable to its fully-supervised counterparts but outperforms existing state-of-the-art weakly-supervised methods in several datasets.

The authors delineate the stages of their approach:

• Point Feature Extraction: Utilizing variations of known architectures, the SQN extracts hierarchical features from point clouds.
• Semantic Query Network Design: A query network retrieves features from local neighborhoods and aids in the broadening of the sparse labels' contextual influence.
• Training Under Sparse Supervision: The system is trained in an end-to-end fashion using a low percentage of annotated points, demonstrating resilience and adaptability in learning complex scene semantics.

Numerical Results and Contributions

Empirical evaluations substantiate the robustness of SQN, with a reported annotation cost reduction of up to 98% without significant degradation in mIoU performance metrics across benchmark datasets like S3DIS, Semantic3D, and SensatUrban. The results emphasize the potential of SQN in real-world applications where annotation resources are scarce.

The research underscores several key contributions:

• An innovative method for weakly-supervised 3D point cloud segmentation that efficiently propagates sparse point labels.
• Insightful empirical evidence demonstrating that full annotation is unnecessary for high accuracy.
• A simplification of the training pipeline relative to existing methods, removing the need for complex pre- or post-processing steps.

Implications and Future Perspectives

Practically, this work opens avenues for deploying 3D semantic segmentation models across real-world applications such as autonomous driving, urban planning, and AR/VR environments. Theoretically, it highlights the merit of exploring further weak supervision strategies and the potential benefits of exploiting innate data characteristics in machine learning models.

Moving forward, the potential exploration areas could include extension towards instance or panoptic segmentation tasks and the integration of active learning mechanisms to enhance annotation effectiveness intelligently. The SQN-based strategies could additionally be adapted for interactive and adaptive learning environments where label efficiency is paramount.

In conclusion, the SQN represents a meaningful advancement in the field of weakly-supervised learning for large-scale 3D point clouds, providing practical solutions to otherwise daunting data annotation challenges while maintaining competitive performance. Its implications and methodologies pave the way for more accessible and scalable developments in AI-driven point-cloud processing.