Li-Cheng Liu

Vision–language guided semantic-geometric transformer for memory-efficient 3D scene understanding

This work proposes a segmentation framework guided by Contrastive Language–Image Pre-training (CLIP) that enriches sparse 3D tokens with vision–language semantic priors and introduces a decoupled CLIP-induced semantic residual that forms semantic-geometric attention biases for local window attention.