EXPLORING LONG-SHORT-TERM CONTEXT FOR POINT CLOUD SEMANTIC SEGMENTATION

Point cloud semantic segmentation attracts numerous attention following the success of the point-based convolution neural network. Due to the ambiguity of the point-based feature, many methods study on integrating contextual information to solve the ambiguous problem. However, the extracted context is severely limited to the small input blocks. Few prior works exploit contextual information beyond the blocks to capture long-range dependencies. To address this limitation, we propose a novel long-short-term context framework, which adopts a long-short-term feature bank to exploit both the local context within each block and the long-range context beyond the current task block. The proposed framework is flexible and easy to be combined with existing models, thereby enables existing models to capture the larger range context. Extensive experiments demonstrate that the proposed model achieves improved segmentation performance, and augmenting existing models with a long-short-term feature bank consistently increases the performance.