Construct to Associate: Cooperative Context Learning for Domain Adaptive Point Cloud Segmentation

Guangrui Li; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 27917-27926

This paper tackles the domain adaptation problem in point cloud semantic segmentation which performs adaptation from a fully labeled domain (source domain) to an unlabeled target domain. Due to the unordered property of point clouds LiDAR scans typically show varying geometric structures across different regions in terms of density noises etc hence leading to increased dynamics on context. However such characteristics are not consistent across domains due to the difference in sensors environments etc thus hampering the effective scene comprehension across domains. To solve this we propose Cooperative Context Learning that performs context modeling and modulation from different aspects but in a cooperative manner. Specifically we first devise context embeddings to discover and model contextual relationships with close neighbors in a learnable manner. Then with the context embeddings from two domains we introduce a set of learnable prototypes to attend and associate them under the attention paradigm. As a result these prototypes naturally establish long-range dependency across regions and domains thereby encouraging the transfer of context knowledge and easing the adaptation. Moreover the attention in turn attunes and guides the local context modeling and urges them to focus on the domain-invariant context knowledge thus promoting the adaptation in a cooperative manner. Experiments on representative benchmarks verify that our method attains the new state-of-the-art.