Cross-Vehicle 3D Geometric Consistency for Self-Supervised Surround Depth Estimation on Articulated Vehicles
Summary: arXiv:2604.02639v1 Announce Type: cross
Surround depth estimation has emerged as a cost-effective alternative to traditional LiDAR technology for 3D perception in the realm of autonomous driving. Recent advancements in self-supervised methodologies have primarily focused on multi-camera setups to enhance scale awareness and scene coverage. However, these methods are predominantly tailored for passenger vehicles and often overlook the complexities associated with articulated vehicles or robotics platforms.
The articulated structure of such vehicles introduces intricate cross-segment geometry and motion coupling, which significantly complicates consistent depth reasoning across various views. In response to these challenges, we present ArticuSurDepth, a novel self-supervised framework specifically designed for surround-view depth estimation on articulated vehicles. Our approach aims to enhance depth learning by leveraging cross-view and cross-vehicle geometric consistency, guided by structural priors derived from vision foundation models.
Key Features of ArticuSurDepth
- Multi-view Spatial Context Enrichment: Our framework employs a strategy that enriches spatial context across multiple views, which is crucial for maintaining structural coherence in depth estimation.
- Cross-view Surface Normal Constraint: This innovative constraint is integrated to bolster structural consistency across both spatial and temporal contexts, ensuring that depth estimation remains accurate regardless of the viewpoint.
- Camera Height Regularization: By incorporating ground plane awareness, we encourage metric depth estimation that is vital for practical applications in autonomous navigation.
- Cross-vehicle Pose Consistency: This feature bridges the motion estimation between articulated segments, enhancing the overall reliability of our depth estimation process.
Experimental Validation
To validate the effectiveness of our proposed method, we established an articulated vehicle experimentation platform and collected a comprehensive dataset for testing. The results from our experiments demonstrate state-of-the-art (SoTA) performance in depth estimation, both on our self-collected dataset and established benchmarks including DDAD, nuScenes, and KITTI.
The implications of our findings are significant for the future of autonomous vehicle technology. By addressing the unique challenges posed by articulated vehicles, ArticuSurDepth represents a substantial advancement in the field of self-supervised depth estimation. This framework not only enhances the accuracy of depth perception but also contributes to the broader objective of safe and reliable autonomous navigation.
In conclusion, our work underscores the importance of developing tailored solutions for diverse vehicle architectures in the pursuit of effective 3D perception technologies. As the field continues to evolve, the integration of innovative approaches like ArticuSurDepth will be crucial in overcoming existing limitations and paving the way for the next generation of autonomous vehicles.