LaserMix for Semi-Supervised LiDAR Semantic Segmentation (2207.00026v4)

Abstract: Densely annotating LiDAR point clouds is costly, which restrains the scalability of fully-supervised learning methods. In this work, we study the underexplored semi-supervised learning (SSL) in LiDAR segmentation. Our core idea is to leverage the strong spatial cues of LiDAR point clouds to better exploit unlabeled data. We propose LaserMix to mix laser beams from different LiDAR scans, and then encourage the model to make consistent and confident predictions before and after mixing. Our framework has three appealing properties: 1) Generic: LaserMix is agnostic to LiDAR representations (e.g., range view and voxel), and hence our SSL framework can be universally applied. 2) Statistically grounded: We provide a detailed analysis to theoretically explain the applicability of the proposed framework. 3) Effective: Comprehensive experimental analysis on popular LiDAR segmentation datasets (nuScenes, SemanticKITTI, and ScribbleKITTI) demonstrates our effectiveness and superiority. Notably, we achieve competitive results over fully-supervised counterparts with 2x to 5x fewer labels and improve the supervised-only baseline significantly by 10.8% on average. We hope this concise yet high-performing framework could facilitate future research in semi-supervised LiDAR segmentation. Code is publicly available.

• The paper introduces LaserMix, a novel semi-supervised learning framework for LiDAR semantic segmentation that leverages spatial priors and laser beams across scans for improved performance.
• LaserMix achieves state-of-the-art results on datasets like nuScenes and SemanticKITTI, improving mIoU by up to 10.8% and requiring 2x to 5x fewer labels than fully supervised methods.
• This approach has significant practical implications for industries like autonomous driving by reducing annotation costs and enabling efficient, scalable deployment of LiDAR segmentation models.

An Analysis of "LaserMix for Semi-Supervised LiDAR Semantic Segmentation"

The paper presents LaserMix, a novel semi-supervised learning (SSL) approach tailored for LiDAR semantic segmentation, addressing the inherent challenges posed by the labor-intensive and expensive nature of annotating LiDAR point clouds. The methodology leverages the unique spatial characteristics and strong spatial priors present in LiDAR data, which are often underscored in traditional 2D/3D semantic segmentation tasks. The primary innovation lies in the efficient utilization of laser beams across different LiDAR scans to improve performance in both low-data and high-data regimes.

Key Contributions

1. Generic Framework: LaserMix is positioned as a universal SSL framework applicable to multiple LiDAR representations, such as range view and voxel. This adaptability is crucial as it permits the method to be employed across various LiDAR platforms without modification.
2. Theoretical Foundation: The authors provide a statistical grounding for their method, offering detailed theoretical insights into why the use of spatial priors with LaserMix improves performance in semi-supervised scenarios.
3. Performance Gains: The approach demonstrates significant enhancements over state-of-the-art (SoTA) methods. On datasets like nuScenes, SemanticKITTI, and ScribbleKITTI, LaserMix outperforms fully supervised models with markedly fewer labels, reporting improvements of up to 10.8% and requiring 2x to 5x fewer labels.

Methodology

The crux of LaserMix's approach is the partitioning of LiDAR scans into coherent spatial areas by leveraging known laser beam geometries. This spatial partitioning is insightful because the distribution of objects within a LiDAR point cloud is mostly consistent concerning the spatial position relative to the sensor. The method mixes beams from different scans, enabling the model to achieve consistent and high-certainty predictions both pre- and post-mixing. This mix-matching of data is computationally efficient, reducing the reliance on labeled datasets and allowing the model to generalize better from fewer annotations.

Empirical Results

LaserMix's effectiveness is validated through comprehensive experimentation showing profound improvements under various labeled data regimes (e.g., 1%, 10%, 20%, and 50% of data). Notably, results illustrate superior performance in critical LiDAR benchmarks:

• On nuScenes, 20% labeled data facilitated a performance increase of 7.9% in mIoU.
• For SemanticKITTI, a 5.2% improvement was noted.
• On the ScribbleKITTI set, 6.7% mIoU improvements were noted with only 0.8% labeled data.

These results indicate LaserMix significantly boosts segmentation quality without an equivalent increase in annotation cost.

Theoretical and Practical Implications

The theoretical underpinning for LaserMix opens new pathways in SSL strategies by suggesting semi-supervised methods can benefit from domain-specific data characteristics like those in LiDAR. Practically, the method promises implications for industries relying on LiDAR data, such as autonomous driving and robotics, which are sensitive to the high costs of data annotation. This research advocates for a deep dive into spatial priors' impact on other multimodal datasets, potentially facilitating new SSL methodologies beyond simple data augmentation.

Future Directions

Potential future explorations could include refining spatial partitioning strategies, incorporating more complex models into the existing framework, and possibly expanding the method's application to other volumetric and temporal data representations. The paper lays a foundation for such developments by proving that spatial patterns, used adequately, play a decisive role in deploying efficient and scalable SSL models with LiDAR systems.

In summary, this work makes a meaningful contribution to the field of LiDAR semantic segmentation, emphasizing the effective use of spatial data characteristics within SSL architectures. The strategy presented by LaserMix may catalyze further research into optimizing semi-supervised techniques across other data-dependent domains.