MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving (2303.08600v1)

Abstract: LiDAR and camera are two modalities available for 3D semantic segmentation in autonomous driving. The popular LiDAR-only methods severely suffer from inferior segmentation on small and distant objects due to insufficient laser points, while the robust multi-modal solution is under-explored, where we investigate three crucial inherent difficulties: modality heterogeneity, limited sensor field of view intersection, and multi-modal data augmentation. We propose a multi-modal 3D semantic segmentation model (MSeg3D) with joint intra-modal feature extraction and inter-modal feature fusion to mitigate the modality heterogeneity. The multi-modal fusion in MSeg3D consists of geometry-based feature fusion GF-Phase, cross-modal feature completion, and semantic-based feature fusion SF-Phase on all visible points. The multi-modal data augmentation is reinvigorated by applying asymmetric transformations on LiDAR point cloud and multi-camera images individually, which benefits the model training with diversified augmentation transformations. MSeg3D achieves state-of-the-art results on nuScenes, Waymo, and SemanticKITTI datasets. Under the malfunctioning multi-camera input and the multi-frame point clouds input, MSeg3D still shows robustness and improves the LiDAR-only baseline. Our code is publicly available at \url{https://github.com/jialeli1/lidarseg3d}.

• The paper introduces a novel multi-modal fusion architecture that jointly learns LiDAR and camera features to overcome modality heterogeneity.
• It employs a three-phased fusion strategy combining geometry-based alignment, cross-modal feature completion, and semantic attention to enhance segmentation performance.
• Strong numerical results on nuScenes, Waymo, and SemanticKITTI validate its effectiveness in accurately segmenting challenging objects for autonomous driving.

An Overview of "MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving"

The paper presents MSeg3D, a sophisticated approach to multi-modal 3D semantic segmentation, designed specifically for autonomous driving platforms utilizing LiDAR and camera data. The core focus is on resolving the challenges inherent in combining multi-modal data to improve segmentation versus LiDAR-only approaches. This work addresses issues such as "modality heterogeneity," limited sensor field view overlap, and inadequacies in multi-modal data augmentation schemes.

Methodological Contributions

1. Joint Intra-modal and Inter-modal Feature Fusion: The proposed technique mitigates modality heterogeneity by integrating intra-modal feature extraction with inter-modal feature fusion. This strategy hinges on simultaneous learning of LiDAR and camera features, encouraging the extraction of features that are both correlated and complementary between the two data sources.
2. Enhanced Multimodal Fusion Design: MSeg3D employs a three-phased fusion mechanism:
   • Geometry-based Fusion (GF-Phase): Aims at aligning LiDAR features with features from camera images based on explicit spatial correspondences.
   • Cross-modal Feature Completion: Completes missing features in camera data using LiDAR data, particularly useful for points outside the camera's field of view.
   • Semantic-based Fusion (SF-Phase): Implements attention mechanisms to model complex semantic interactions between modalities, improving segmentation for areas both within and beyond the intersection of the sensor fields.
3. Asymmetric Multi-modal Data Augmentation: Overcoming the challenge of data augmentation across modalities, the method proposes asymmetric transformations, applied specifically to each modality, maximizing the heterogeneity of training data and enhancing robustness.

Strong Numerical Outcomes and Claims

The proposed MSeg3D model outperforms previous single and multi-modal approaches, as demonstrated by its leading performance on nuScenes, Waymo, and SemanticKITTI datasets. This is notable in its robust performance across different sensor configurations and object sizes, including small and distant objects that typically pose challenges for LiDAR-only models.

Implications for Autonomous Driving and Beyond

Practical Implications: The ability of MSeg3D to effectively integrate modalities promises improvements in safety and perception accuracy for autonomous driving systems. Specifically, its design effectively deals with variable conditions and sparse data environments, offering a significant step forward in the deployment of real-world autonomous vehicles.

Theoretical Implications: From a conceptual standpoint, this work enhances the discourse on feature extraction and fusion strategies in multi-modal systems, offering a well-rounded architecture that serves as a potential template for future research.

Future Directions: Continuations or expansions of this work could explore real-time processing constraints, as computational efficiency remains a critical consideration for on-vehicle implementations. Moreover, exploring the integration of additional modalities, such as radar, could further strengthen robustness.

In conclusion, the MSeg3D framework marks a significant advance in the field of semantic segmentation for autonomous driving by leveraging the strengths of multi-modal data and sophisticated fusion techniques to overcome the limitations of traditional approaches.