Contrastive Boundary Learning for Point Cloud Segmentation (2203.05272v2)

Abstract: Point cloud segmentation is fundamental in understanding 3D environments. However, current 3D point cloud segmentation methods usually perform poorly on scene boundaries, which degenerates the overall segmentation performance. In this paper, we focus on the segmentation of scene boundaries. Accordingly, we first explore metrics to evaluate the segmentation performance on scene boundaries. To address the unsatisfactory performance on boundaries, we then propose a novel contrastive boundary learning (CBL) framework for point cloud segmentation. Specifically, the proposed CBL enhances feature discrimination between points across boundaries by contrasting their representations with the assistance of scene contexts at multiple scales. By applying CBL on three different baseline methods, we experimentally show that CBL consistently improves different baselines and assists them to achieve compelling performance on boundaries, as well as the overall performance, eg in mIoU. The experimental results demonstrate the effectiveness of our method and the importance of boundaries for 3D point cloud segmentation. Code and model will be made publicly available at https://github.com/LiyaoTang/contrastBoundary.

• The paper introduces Contrastive Boundary Learning (CBL), a novel framework designed to improve accuracy in segmenting boundary regions within 3D point clouds.
• CBL uses contrastive optimization and a sub-scene boundary mining strategy to enhance feature discrimination and capture detailed boundary information at multiple scales.
• Empirical validation on various datasets and baselines demonstrates that CBL consistently improves both boundary-specific metrics (like B-IoU) and overall segmentation performance.

An Overview of Contrastive Boundary Learning for Point Cloud Segmentation

The paper "Contrastive Boundary Learning for Point Cloud Segmentation" addresses the challenges faced in segmenting scene boundaries within three-dimensional (3D) point clouds. Accurately delineating boundaries in 3D environments is fundamental for applications such as autonomous driving and virtual reality. Despite advances in point cloud segmentation techniques, current methodologies often struggle with boundary regions, resulting in degraded overall segmentation performance. This paper introduces a novel approach—contrastive boundary learning (CBL)—designed to specifically enhance segmentation accuracy along these critical boundary areas.

Motivation and Key Contributions

The necessity to improve boundary segmentation arises from the observation that errors in boundary regions can disproportionately affect the recognition of smaller object categories, such as pedestrians or poles, compared to larger entities like buildings or trees. The ability to accurately delineate boundaries is crucial for applications that rely on precise object detection and classification.

This paper makes several key contributions:

1. Metrics for Boundary Performance: The authors propose using mean intersection-over-union (mIoU) computed separately for boundary and inner areas, along with the boundary IoU (B-IoU), to quantify segmentation quality on boundaries more effectively.
2. Contrastive Boundary Learning Framework: The CBL framework enhances feature discrimination around boundaries by leveraging contrastive optimization. It considers the semantic relationship between boundary points and their neighbors, encouraging features of points within the same category to be similar while differentiating those across boundaries.
3. Sub-scene Boundary Mining: A sub-scene boundary mining strategy is applied, leveraging sub-sampling during model training to identify and optimize boundaries at multiple scales. This hierarchical approach ensures that models can capture detailed boundary information throughout different stages of sub-sampling.
4. Empirical Validation: The framework is evaluated on various datasets with multiple baseline methods, including ConvNet, RandLA-Net, and CloserLook3D, demonstrating consistent improvements in both boundary-specific and overall segmentation performance. For instance, applying CBL with RandLA-Net resulted in superior performance on the Semantic3D dataset, and significant gains were reported using ConvNet on the S3DIS dataset.

Experimental Results

The authors quantitatively demonstrate the CBL framework's efficacy through extensive experiments. They highlight how existing methods underperform on boundaries, as indicated by lower mIoU scores in boundary regions compared to inner regions. By applying CBL, boundary-specific metrics, such as B-IoU, showed notable improvement across different baselines.

Key results include:

• Consistent enhancements in mIoU and B-IoU with CBL, with substantial gains particularly on boundary regions.
• Superior performance on multiple large-scale datasets like S3DIS, Semantic3D, and others, supporting the framework's robustness.
• Improved feature discrimination across boundaries, resulting in more accurate scene segmentation.

Implications and Future Directions

The introduction of CBL represents a significant shift in focus towards boundary optimization in point cloud segmentation. By drawing attention to boundary regions, the research underscores the importance of feature discrimination in enhancing overall segmentation quality. In practical scenarios, such advancements can lead to safer and more reliable autonomous systems that require precise environmental understanding.

Future work could potentially explore integrating CBL with emerging neural architectures, such as transformers, and expanding the method to handle dynamic scenes. Additionally, refining the sub-scene boundary mining approach to further improve computational efficiency and generalization across diverse 3D environments would be valuable. Exploring the interplay between boundary features and semantic context could also yield innovative approaches in finetuning segmentation models for real-time applications.