CamPoint: Boosting Point Cloud Segmentation with Virtual Camera

tures, whereby an increased number of shared cameras observing both points corresponds to a reduced distance between them. To effectively facilitate global semantic perception, we assign each camera an optimizable embedding and then integrate these embeddings into the original spatial features based on visibility attributes, thereby obtaining high-level features enriched with camera priors. Additionally, the state space model characterized by linear computational complexity is employed as the operator to achieve global learning with efficiency. Comprehensive experiments on multiple datasets show that our CamPoint surpasses the current state-of-the-art in multiple datasets, achieving low training cost and fast inference speed.