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About this paper
Xiang Xu, Lingdong Kong, Hui Shuai, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu, Qingshan Liu
The SuperFlow framework advances 3D representation learning by effectively leveraging spatiotemporal information from consecutive LiDAR-camera pairs through a novel contrastive learning approach. It achieves state-of-the-art performance on various 3D perception benchmarks, notably improving data efficiency with limited annotations and enhancing robustness to diverse environments and sensor corruptions.
Key insights
- Explicitly leveraging temporal dynamics from consecutive LiDAR scans significantly enhances the robustness and quality of learned 3D representations.
- Employing semantic-related superpixels, generated with Vision Foundation Models, resolves 'self-conflict' issues, improving cross-modal alignment between 2D and 3D data.
- Aligning features from sparse single-frame and dense multi-sweep point clouds effectively leads to density-invariant 3D representations.
Summary by alphaXiv.
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