Weakly-Supervised 3D Visual Grounding based on Visual Linguistic Alignment

arXiv:2312.09625v2 Announce Type: replace
Abstract: Learning to ground natural language queries to target objects or regions in 3D point clouds is quite essential for 3D scene understanding. Nevertheless, existing 3D visual grounding approaches require a substantial number of bounding box annotations for text queries, which is time-consuming and labor-intensive to obtain. In this paper, we propose \textbf{3D-VLA}, a weakly supervised approach for \textbf{3D} visual grounding based on \textbf{V}isual \textbf{L}inguistic \textbf{A}lignment. Our 3D-VLA exploits the superior ability of current large-scale vision-language …

abstract alignment annotations arxiv box cs.cl cs.cv labor language natural natural language natural language queries objects queries text type understanding visual weakly-supervised