4D-CS: Exploiting Cluster Prior for 4D Spatio-Temporal LiDAR Semantic Segmentation (2501.02937v1)

Abstract: Semantic segmentation of LiDAR points has significant value for autonomous driving and mobile robot systems. Most approaches explore spatio-temporal information of multi-scan to identify the semantic classes and motion states for each point. However, these methods often overlook the segmentation consistency in space and time, which may result in point clouds within the same object being predicted as different categories. To handle this issue, our core idea is to generate cluster labels across multiple frames that can reflect the complete spatial structure and temporal information of objects. These labels serve as explicit guidance for our dual-branch network, 4D-CS, which integrates point-based and cluster-based branches to enable more consistent segmentation. Specifically, in the point-based branch, we leverage historical knowledge to enrich the current feature through temporal fusion on multiple views. In the cluster-based branch, we propose a new strategy to produce cluster labels of foreground objects and apply them to gather point-wise information to derive cluster features. We then merge neighboring clusters across multiple scans to restore missing features due to occlusion. Finally, in the point-cluster fusion stage, we adaptively fuse the information from the two branches to optimize segmentation results. Extensive experiments confirm the effectiveness of the proposed method, and we achieve state-of-the-art results on the multi-scan semantic and moving object segmentation on SemanticKITTI and nuScenes datasets. The code will be available at https://github.com/NEU-REAL/4D-CS.git.

• The paper introduces 4D-CS, a dual-branch network leveraging cluster priors and temporal fusion modules to enhance consistent 4D LiDAR semantic segmentation.
• Evaluations on SemanticKITTI and nuScenes datasets show state-of-the-art performance, improving mean IoU, especially for challenging object categories.
• This approach significantly enhances the ability of autonomous systems to maintain semantic integrity over time and space in complex dynamic environments.

Exploring 4D-CS: Cluster-Based Spatio-Temporal LiDAR Semantic Segmentation

The paper presented by Jiexi Zhong et al., introduces an advanced approach for LiDAR semantic segmentation, focusing on the utilization of cluster priors to enhance segmentation consistency over space and time. Their method, 4D-CS, is a dual-branch network that leverages both spatial and temporal information from LiDAR data and integrates it with clustering strategies to improve the accuracy of object recognition in autonomous driving contexts.

Key Contributions

1. Dual-Branch Network: The architecture of 4D-CS is designed with a dual-branch network that addresses the common issue where points belonging to the same object may be inconsistently classified across different frames. The point-based branch and the newly introduced cluster-based branch function synergistically, with cluster labels providing an innovative solution for segmentation consistency.
2. Multi-View Temporal Fusion (MTF): This module enriches point features by incorporating historical data. Unlike previous approaches that might accumulate noise over time, MTF utilizes the most recent multi-perspective data to enhance the current spatial feature set, avoiding erroneous feature propagation.
3. Temporal Cluster Enhancement (TCE): Clustering is applied to improve the spatial and temporal coverage of segmentation. TCE retrieves cluster features from past frames to mitigate occlusion and sparsity artifacts in LiDAR data, enriching the instance-level information that can be captured over time.
4. Adaptive Prediction Fusion (APF): This module serves to optimally combine the results of the point-based and cluster-based branches. Through an adaptive method, APF leverages the strengths of each branch to balance consistency and accuracy in segmentation outputs.

Experimental Results

Experimental assessments were conducted on the SemanticKITTI and nuScenes datasets, prominent benchmarks for autonomous driving scenarios. The 4D-CS achieved state-of-the-art results in both semantic segmentation and moving object segmentation tasks.

• SemanticKITTI Dataset: The proposed method demonstrated a notable improvement in mean IoU scores, particularly for challenging categories such as large vehicles, indicating the effectiveness of the cluster-based approach in handling complex occlusions and object dynamics.
• nuScenes Dataset: Performance improvements were consistent across various classes, further indicating the robustness of the 4D-CS model in different urban environments and sensor settings.

Implications and Future Directions

The 4D-CS method showcases significant advancements in handling dynamic and complex urban scenes for autonomous vehicles. The integration of cluster information introduces a reliable mechanism for maintaining semantic integrity over time and space. Practically, this could improve navigation systems' ability to interact with dynamic real-world conditions, laying a pathway towards more intelligent and reliable autonomous systems.

From a theoretical standpoint, the approach opens potential research avenues in refining clustering techniques to incorporate more context-aware information in real-time environments. Furthermore, exploring adaptive learning techniques that can leverage data from multiple sources could enhance temporal fusion modules, making the method applicable to even more diverse scenarios.

Looking ahead, improvements could be achieved by integrating this method with multisensor data, potentially combining visual, auditory, and textual information to foster even more comprehensive understanding in spatio-temporal domains. Additionally, real-world deployment would necessitate attentiveness to memory efficiency and computation time to ensure seamless integration with existing systems. As the field progresses, 4D-CS sets a solid foundation for developing more sophisticated models that venture beyond traditional segmentation boundaries.